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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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Shi T, Gao Y, Song J, Ao M, Hu X, Yang W, Chen W, Liu Y, Feng H. Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains. Food Chem 2024; 461:140651. [PMID: 39154465 DOI: 10.1016/j.foodchem.2024.140651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/02/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024]
Abstract
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.
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Affiliation(s)
- Taotao Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Yuan Gao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Min Ao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China
| | - Yanyan Liu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, Hubei, PR China.
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Karim MJ, Goni MOF, Nahiduzzaman M, Ahsan M, Haider J, Kowalski M. Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM. Sci Rep 2024; 14:16022. [PMID: 38992069 PMCID: PMC11239930 DOI: 10.1038/s41598-024-66989-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/08/2024] [Indexed: 07/13/2024] Open
Abstract
Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.
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Affiliation(s)
- Md Jawadul Karim
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York, YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, 00-908, Warsaw, Poland.
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Lapajne J, Vojnović A, Vončina A, Žibrat U. Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications. PLANTS (BASEL, SWITZERLAND) 2024; 13:1918. [PMID: 39065444 PMCID: PMC11281287 DOI: 10.3390/plants13141918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/04/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR-Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC-Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475-580 nm, 660-730 nm, 940-970 nm, 1420-1510 nm, 1875-2040 nm, and 2350-2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.
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Affiliation(s)
- Janez Lapajne
- Plant Protection Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia; (A.V.); (U.Ž.)
| | - Ana Vojnović
- Crop Science Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia;
| | - Andrej Vončina
- Plant Protection Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia; (A.V.); (U.Ž.)
| | - Uroš Žibrat
- Plant Protection Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia; (A.V.); (U.Ž.)
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Kaushal S, Gill HS, Billah MM, Khan SN, Halder J, Bernardo A, Amand PS, Bai G, Glover K, Maimaitijiang M, Sehgal SK. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning. FRONTIERS IN PLANT SCIENCE 2024; 15:1410249. [PMID: 38872880 PMCID: PMC11169824 DOI: 10.3389/fpls.2024.1410249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 05/06/2024] [Indexed: 06/15/2024]
Abstract
Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.
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Affiliation(s)
- Swas Kaushal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Harsimardeep S. Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Mohammad Maruf Billah
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Shahid Nawaz Khan
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Amy Bernardo
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Paul St. Amand
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Guihua Bai
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Karl Glover
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Maitiniyazi Maimaitijiang
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Sunish K. Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
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Ahmad A, Liew AXW, Venturini F, Kalogeras A, Candiani A, Di Benedetto G, Ajibola S, Cartujo P, Romero P, Lykoudi A, De Grandis MM, Xouris C, Lo Bianco R, Doddy I, Elegbede I, D'Urso Labate GF, García del Moral LF, Martos V. AI can empower agriculture for global food security: challenges and prospects in developing nations. Front Artif Intell 2024; 7:1328530. [PMID: 38726306 PMCID: PMC11081032 DOI: 10.3389/frai.2024.1328530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/11/2024] [Indexed: 05/12/2024] Open
Abstract
Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.
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Affiliation(s)
- Ali Ahmad
- Research Institute for Integrated Coastal Zone Management, Polytechnic University of Valencia, Grau de Gandia, Valencia, Spain
| | | | - Francesca Venturini
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Winterthur, Switzerland
- TOELT LLC, Dübendorf, Switzerland
| | | | | | | | - Segun Ajibola
- Afridat UG, Bonn, Germany
- NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal
| | - Pedro Cartujo
- Department of Electronic and Computer Technology, University of Granada, Granada, Spain
| | - Pablo Romero
- GRANIOT Satellite Technologies S.L, Granada, Spain
| | | | | | - Christos Xouris
- Gaia Robotics Idiotiki Kefalaiouxiki Etaireia, Patras, Greece
| | - Riccardo Lo Bianco
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Palermo, Italy
| | - Irawan Doddy
- Department of Mechanical Engineering, Universitas Muhammadiyah Pontianak – Universitas, Kalimantan Barat, Indonesia
| | | | | | - Luis F. García del Moral
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
| | - Vanessa Martos
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
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Ahmed N, Zhang B, Deng L, Bozdar B, Li J, Chachar S, Chachar Z, Jahan I, Talpur A, Gishkori MS, Hayat F, Tu P. Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation-a review. FRONTIERS IN PLANT SCIENCE 2024; 15:1357153. [PMID: 38685958 PMCID: PMC11057267 DOI: 10.3389/fpls.2024.1357153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/20/2024] [Indexed: 05/02/2024]
Abstract
Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.
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Affiliation(s)
- Nazir Ahmed
- College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Baige Zhang
- Key Laboratory for New Technology Research of Vegetables, Vegetable Research Institute, Guangdong Academy of Agricultural Science, Guangzhou, China
| | - Lansheng Deng
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China
| | - Bilquees Bozdar
- Faculty of Crop Production, Sindh Agriculture University, Tandojam, Pakistan
| | - Juan Li
- College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Sadaruddin Chachar
- College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Zaid Chachar
- College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Itrat Jahan
- Faculty of Crop Production, Sindh Agriculture University, Tandojam, Pakistan
| | - Afifa Talpur
- Faculty of Crop Production, Sindh Agriculture University, Tandojam, Pakistan
| | | | - Faisal Hayat
- College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Panfeng Tu
- College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
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Qu H, Zheng C, Ji H, Huang R, Wei D, Annis S, Drummond F. A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity. FRONTIERS IN PLANT SCIENCE 2024; 15:1340884. [PMID: 38606063 PMCID: PMC11007028 DOI: 10.3389/fpls.2024.1340884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/26/2024] [Indexed: 04/13/2024]
Abstract
Introduction Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Methods Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Results Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. Discussion These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.
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Affiliation(s)
- Hongchun Qu
- Institute of Ecological Safety and College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
- College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Chaofang Zheng
- Institute of Ecological Safety and College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Hao Ji
- Institute of Ecological Safety and College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
- College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Rui Huang
- College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Dianwen Wei
- Institute of Natural Resources and Ecology, Heilongjiang Academy of Sciences, Harbin, China
| | - Seanna Annis
- School of Biology and Ecology, University of Maine, Orono, ME, United States
- Cooperative Extension, University of Maine, Orono, ME, United States
| | - Francis Drummond
- School of Biology and Ecology, University of Maine, Orono, ME, United States
- Cooperative Extension, University of Maine, Orono, ME, United States
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9
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Trippa D, Scalenghe R, Basso MF, Panno S, Davino S, Morone C, Giovino A, Oufensou S, Luchi N, Yousefi S, Martinelli F. Next-generation methods for early disease detection in crops. PEST MANAGEMENT SCIENCE 2024; 80:245-261. [PMID: 37599270 DOI: 10.1002/ps.7733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/21/2023] [Indexed: 08/22/2023]
Abstract
Plant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. In this review, the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations are presented and discussed. Traditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme-linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high-throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non-cultivable pathogens in the laboratory, nucleic acid-based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last-generation portable thermocyclers may provide rapid detection results in situ. The advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Daniela Trippa
- Dipartimento di Scienze Agrarie Alimentari e Forestali, Università degli Studi di Palermo, Palermo, Italy
| | - Riccardo Scalenghe
- Dipartimento di Scienze Agrarie Alimentari e Forestali, Università degli Studi di Palermo, Palermo, Italy
| | | | - Stefano Panno
- Dipartimento di Scienze Agrarie Alimentari e Forestali, Università degli Studi di Palermo, Palermo, Italy
| | - Salvatore Davino
- Dipartimento di Scienze Agrarie Alimentari e Forestali, Università degli Studi di Palermo, Palermo, Italy
| | - Chiara Morone
- Regione Piemonte - Phytosanitary Division, Torino, Italy
| | - Antonio Giovino
- Council for Agricultural Research and Economics (CREA)-Research Centre for Plant Protection and Certification (CREA-DC), Palermo, Italy
| | - Safa Oufensou
- Dipartimento di Agraria, Università degli Studi di Sassari, Sassari, Italy
| | - Nicola Luchi
- National Research Council, Institute for Sustainable Plant Protection, (CNR-IPSP), Florence, Italy
| | - Sanaz Yousefi
- Department of Horticultural Science, Bu-Ali Sina University, Hamedan, Iran
| | - Federico Martinelli
- Department of Biology, University of Florence, Florence, Italy
- National Research Council, Institute for Sustainable Plant Protection, (CNR-IPSP), Florence, Italy
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10
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Okyere FG, Cudjoe D, Sadeghi-Tehran P, Virlet N, Riche AB, Castle M, Greche L, Simms D, Mhada M, Mohareb F, Hawkesford MJ. Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1209500. [PMID: 37908836 PMCID: PMC10613979 DOI: 10.3389/fpls.2023.1209500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/05/2023] [Indexed: 11/02/2023]
Abstract
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.
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Affiliation(s)
- Frank Gyan Okyere
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | - Daniel Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | | | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Latifa Greche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Daniel Simms
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | - Manal Mhada
- AgroBioSciences Department, University of Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
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11
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Mostafa S, Mondal D, Panjvani K, Kochian L, Stavness I. Explainable deep learning in plant phenotyping. Front Artif Intell 2023; 6:1203546. [PMID: 37795496 PMCID: PMC10546035 DOI: 10.3389/frai.2023.1203546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 10/06/2023] Open
Abstract
The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.
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Affiliation(s)
- Sakib Mostafa
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Debajyoti Mondal
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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12
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Dhaka VS, Kundu N, Rani G, Zumpano E, Vocaturo E. Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7877. [PMID: 37765934 PMCID: PMC10537018 DOI: 10.3390/s23187877] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 09/29/2023]
Abstract
The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.
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Affiliation(s)
- Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India;
| | - Nidhi Kundu
- Sri Karan Narendra Agriculture, Jobner 303328, India;
| | - Geeta Rani
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India;
| | - Ester Zumpano
- Department of Informatics, Modeling Electronics and Systems (DIMES), University of Calabria, Arcavacata di Rende, 87036 Rende, Italy; (E.Z.); (E.V.)
- National Research Council-Institute of Nanotechnology, Piazzale Aldo Moro, 33C, Arcavacata, 87036 Rome, Italy
| | - Eugenio Vocaturo
- Department of Informatics, Modeling Electronics and Systems (DIMES), University of Calabria, Arcavacata di Rende, 87036 Rende, Italy; (E.Z.); (E.V.)
- National Research Council-Institute of Nanotechnology, Piazzale Aldo Moro, 33C, Arcavacata, 87036 Rome, Italy
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13
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Malinverno L, Barros V, Ghisoni F, Visonà G, Kern R, Nickel PJ, Ventura BE, Šimić I, Stryeck S, Manni F, Ferri C, Jean-Quartier C, Genga L, Schweikert G, Lovrić M, Rosen-Zvi M. A historical perspective of biomedical explainable AI research. PATTERNS (NEW YORK, N.Y.) 2023; 4:100830. [PMID: 37720333 PMCID: PMC10500028 DOI: 10.1016/j.patter.2023.100830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.
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Affiliation(s)
| | - Vesna Barros
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
| | | | - Giovanni Visonà
- Empirical Inference, Max-Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | - Roman Kern
- Institute of Interactive Systems and Data Science, Graz University of Technology, Sandgasse 36/III, 8010 Graz, Austria
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Philip J. Nickel
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | | | - Ilija Šimić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Sarah Stryeck
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 138010 Graz, Austria
| | | | - Cesar Ferri
- VRAIN, Universitat Politècnica de València, Camino de Vera, s/n 46022 Valencia, Spain
| | - Claire Jean-Quartier
- Research Data Management, Graz University of Technology, Brockmanngasse 84, 8010 Graz, Austria
| | - Laura Genga
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | - Gabriele Schweikert
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Mario Lovrić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
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14
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Wang T, Xu Z, Hu H, Xu H, Zhao Y, Mao X. Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial-Spectral Attention 3DCNN and a Transformer. Molecules 2023; 28:6427. [PMID: 37687257 PMCID: PMC10490299 DOI: 10.3390/molecules28176427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
Turtle shell (Chinemys reecesii) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72-2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral-spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial-spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model's capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA-3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells.
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Affiliation(s)
- Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (T.W.); (Z.X.); (H.H.)
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (T.W.); (Z.X.); (H.H.)
| | - Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (T.W.); (Z.X.); (H.H.)
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (T.W.); (Z.X.); (H.H.)
| | - Yuping Zhao
- China Academy of Chinese Medical Sciences, Beijing 100700, China;
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (T.W.); (Z.X.); (H.H.)
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou 450001, China
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15
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Sarkar S, Zhou J, Scaboo A, Zhou J, Aloysius N, Lim TT. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning. PLANTS (BASEL, SWITZERLAND) 2023; 12:2893. [PMID: 37631105 PMCID: PMC10458648 DOI: 10.3390/plants12162893] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023]
Abstract
Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively.
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Affiliation(s)
- Shagor Sarkar
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (S.S.)
| | - Jing Zhou
- Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Andrew Scaboo
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (S.S.)
| | - Jianfeng Zhou
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (S.S.)
| | - Noel Aloysius
- Department of Chemical & Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA
| | - Teng Teeh Lim
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (S.S.)
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16
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Qamar F, Dobler G. Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing. PLANT METHODS 2023; 19:74. [PMID: 37516859 PMCID: PMC10385980 DOI: 10.1186/s13007-023-01046-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/30/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4-1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters. RESULTS We propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r2 of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method. CONCLUSIONS We propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.
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Affiliation(s)
- Farid Qamar
- Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, 19716, USA.
- Data Science Institute, University of Delaware, Newark, DE, 19716, USA.
- Biden School of Public Policy and Administration, University of Delaware, Newark, DE, 19716, USA.
| | - Gregory Dobler
- Data Science Institute, University of Delaware, Newark, DE, 19716, USA
- Biden School of Public Policy and Administration, University of Delaware, Newark, DE, 19716, USA
- Department of Physics and Astronomy, University of Delaware, Newark, DE, 19716, USA
- Center for Urban Science and Progress, New York University, New York, NY, 10003, USA
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17
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Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E. Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. FRONTIERS IN PLANT SCIENCE 2023; 14:1178902. [PMID: 37546247 PMCID: PMC10401275 DOI: 10.3389/fpls.2023.1178902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | | | | | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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18
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Ahn E, Fall C, Botkin J, Curtin S, Prom LK, Magill C. Inoculation and Screening Methods for Major Sorghum Diseases Caused by Fungal Pathogens: Claviceps africana, Colletotrichum sublineola, Sporisorium reilianum, Peronosclerospora sorghi and Macrophomina phaseolina. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091906. [PMID: 37176964 PMCID: PMC10180756 DOI: 10.3390/plants12091906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/03/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Sorghum is the fifth most important crop globally. Researching interactions between sorghum and fungal pathogens is essential to further elucidate plant defense mechanisms to biotic stress, which allows breeders to employ genetic resistance to disease. A variety of creative and useful inoculation and screening methods have been developed by sorghum pathologists to study major fungal diseases. As inoculation and screening methods can be keys for successfully conducting experiments, it is necessary to summarize the techniques developed by this research community. Among many fungal pathogens of sorghum, here we summarize inoculation and screening methods for five important fungal pathogens of sorghum: Claviceps africana, Colletotrichum sublineola, Sporisorium reilianum, Peronosclerospora sorghi and Macrophomina phaseolina. The methods described within will be useful for researchers who are interested in exploring sorghum-fungal pathogen interactions. Finally, we discuss the latest biotechnologies and methods for studying plant-fungal pathogen interactions and their applicability to sorghum pathology.
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Affiliation(s)
- Ezekiel Ahn
- USDA-ARS Plant Science Research Unit, St. Paul, MN 55108, USA
| | - Coumba Fall
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA
| | - Jacob Botkin
- Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108, USA
| | - Shaun Curtin
- USDA-ARS Plant Science Research Unit, St. Paul, MN 55108, USA
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
- Center for Plant Precision Genomics, University of Minnesota, St. Paul, MN 55108, USA
- Center for Genome Engineering, University of Minnesota, St. Paul, MN 55108, USA
| | - Louis K Prom
- USDA-ARS Southern Plains Agricultural Research Center, College Station, TX 77845, USA
| | - Clint Magill
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA
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19
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Young TJ, Jubery TZ, Carley CN, Carroll M, Sarkar S, Singh AK, Singh A, Ganapathysubramanian B. "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies. FRONTIERS IN PLANT SCIENCE 2023; 14:1141153. [PMID: 37063230 PMCID: PMC10090282 DOI: 10.3389/fpls.2023.1141153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as 'canopy fingerprints'. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.
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Affiliation(s)
- Therin J. Young
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
| | | | - Clayton N. Carley
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Matthew Carroll
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Translational AI Center, Iowa State University, Ames, IA, United States
| | - Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Translational AI Center, Iowa State University, Ames, IA, United States
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20
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Dhakal K, Sivaramakrishnan U, Zhang X, Belay K, Oakes J, Wei X, Li S. Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels. SENSORS (BASEL, SWITZERLAND) 2023; 23:3523. [PMID: 37050581 PMCID: PMC10098892 DOI: 10.3390/s23073523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/22/2023] [Accepted: 03/25/2023] [Indexed: 06/19/2023]
Abstract
Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.
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Affiliation(s)
- Kshitiz Dhakal
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Upasana Sivaramakrishnan
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Xuemei Zhang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Kassaye Belay
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Graduate Program in Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA 24061, USA
| | - Joseph Oakes
- Virginia Tech Eastern Virginia Agricultural Research and Extension Center (AREC), Warsaw, VA 22572, USA
| | - Xing Wei
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
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21
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Chen SY, Hsu SH, Ko CY, Hsu KH. Real-time defect and freshness inspection on chicken eggs using hyperspectral imaging. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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22
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Walsh JJ, Mangina E, Negrão S. Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 6:0153. [PMID: 38435466 PMCID: PMC10905704 DOI: 10.34133/plantphenomics.0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/27/2024] [Indexed: 03/05/2024]
Abstract
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
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Affiliation(s)
- Jason John Walsh
- School of Biology & Environmental Science,
University College Dublin, Belfield, Dublin, Ireland
- School of Computer Science,
University College Dublin, Belfield, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science,
University College Dublin, Belfield, Dublin, Ireland
| | - Sonia Negrão
- School of Biology & Environmental Science,
University College Dublin, Belfield, Dublin, Ireland
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23
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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24
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Park E, Kim YS, Faqeerzada MA, Kim MS, Baek I, Cho BK. Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng ( Panax ginseng Meyer). FRONTIERS IN PLANT SCIENCE 2023; 14:1109060. [PMID: 36818876 PMCID: PMC9930644 DOI: 10.3389/fpls.2023.1109060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.
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Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Yuseong, Daejeon, Republic of Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
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25
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Jia Y, Shi Y, Luo J, Sun H. Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module. SENSORS (BASEL, SWITZERLAND) 2023; 23:1494. [PMID: 36772533 PMCID: PMC9920900 DOI: 10.3390/s23031494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/16/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three-dimensional-two-dimensional (3D-2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial-spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D-2D hybrid CNN, resulting in a Y-shaped architecture named Y-Net. The results show that the spectral bands selected by the band selection module of Y-Net achieve more reliable classification performance than traditional feature selection methods. Y-Net obtained the best classification accuracy compared to support vector machines, one-dimensional (1D) CNNs, and two-dimensional (2D) CNNs. After the network pruned the trained Y-Net, the model size was reduced to one-third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.
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26
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Balaji V, Anushkannan NK, Narahari SC, Rattan P, Verma D, Awasthi DK, Pandian AA, Veeramanickam MRM, Mulat MB. Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images. CONTRAST MEDIA & MOLECULAR IMAGING 2023; 2023:5644727. [PMID: 37213211 PMCID: PMC10199794 DOI: 10.1155/2023/5644727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Accepted: 08/17/2022] [Indexed: 05/23/2023]
Abstract
Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.
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Affiliation(s)
- V. Balaji
- Department of EEE, Aditya Engineering College, Surampalem, Andhra Pradesh, India
| | - N. K. Anushkannan
- Department of ECE, Kathir College of Engineering, Coimbatore, Tamilnadu, India
| | | | - Punam Rattan
- School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India
| | - Devvret Verma
- Department of Biotechnology, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India
| | | | - A. Anbarasa Pandian
- Department of Computer Science and Engineering, Panimalar Engineering College, Poonamallae, Chennai, Tamilnadu, India
| | - M. R. M. Veeramanickam
- Centre of Excellence for Cyber Security Technologies, Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India
| | - Molla Bayih Mulat
- Department of Chemical Engineering College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
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27
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Brugger A, Yamati FI, Barreto A, Paulus S, Schramowsk P, Kersting K, Steiner U, Neugart S, Mahlein AK. Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites. PHYTOPATHOLOGY 2023; 113:44-54. [PMID: 35904439 DOI: 10.1094/phyto-03-22-0086-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.
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Affiliation(s)
- Anna Brugger
- University of Bonn, Institute for Crop Science and Resource Conservation (INRES)-Plant Pathology, Bonn, 53115, Germany
| | | | - Abel Barreto
- Institute of Sugar Beet Research, Goettingen, 37079, Germany
| | - Stefan Paulus
- Institute of Sugar Beet Research, Goettingen, 37079, Germany
| | - Patrick Schramowsk
- Technical University Darmstadt, Computer Science Department and Centre for Cognitive Science, Darmstadt, 64289, Germany
| | - Kristian Kersting
- Technical University Darmstadt, Computer Science Department and Centre for Cognitive Science, Darmstadt, 64289, Germany
| | - Ulrike Steiner
- University of Bonn, Institute for Crop Science and Resource Conservation (INRES)-Plant Pathology, Bonn, 53115, Germany
| | - Susanne Neugart
- University of Goettingen, Division of Quality and Sensory of Plant Products, Goettingen, 37075, Germany
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28
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Fu J, Liu J, Zhao R, Chen Z, Qiao Y, Li D. Maize disease detection based on spectral recovery from RGB images. FRONTIERS IN PLANT SCIENCE 2022; 13:1056842. [PMID: 36618618 PMCID: PMC9811593 DOI: 10.3389/fpls.2022.1056842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Maize is susceptible to infect pest disease, and early disease detection is key to preventing the reduction of maize yields. The raw data used for plant disease detection are commonly RGB images and hyperspectral images (HSI). RGB images can be acquired rapidly and low-costly, but the detection accuracy is not satisfactory. On the contrary, using HSIs tends to obtain higher detection accuracy, but HSIs are difficult and high-cost to obtain in field. To overcome this contradiction, we have proposed the maize spectral recovery disease detection framework which includes two parts: the maize spectral recovery network based on the advanced hyperspectral recovery convolutional neural network (HSCNN+) and the maize disease detection network based on the convolutional neural network (CNN). Taking raw RGB data as input of the framework, the output reconstructed HSIs are used as input of disease detection network to achieve disease detection task. As a result, the detection accuracy obtained by using the low-cost raw RGB data almost as same as that obtained by using HSIs directly. The HSCNN+ is found to be fit to our spectral recovery model and the reconstruction fidelity was satisfactory. Experimental results demonstrate that the reconstructed HSIs efficiently improve detection accuracy compared with raw RGB image in tested scenarios, especially in complex environment scenario, for which the detection accuracy increases by 6.14%. The proposed framework has the advantages of fast, low cost and high detection precision. Moreover, the framework offers the possibility of real-time and precise field disease detection and can be applied in agricultural robots.
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Affiliation(s)
- Jun Fu
- College of Biological and Agricultural Engineering, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
| | - Jindai Liu
- College of Biological and Agricultural Engineering, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
| | - Rongqiang Zhao
- College of Biological and Agricultural Engineering, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
| | - Zhi Chen
- College of Biological and Agricultural Engineering, Jilin University, Changchun, China
- Department of Science and Technology Development, Chinese Academy of Agricultural Mechanization Sciences, Beijing, China
| | - Yongliang Qiao
- Australian Centre for Field Robotics (ACFR), Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Dan Li
- College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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29
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Jeong S, Jeong S, Bong J. Detection of Tomato Leaf Miner Using Deep Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:9959. [PMID: 36560327 PMCID: PMC9784543 DOI: 10.3390/s22249959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigated two DNN models for classification and segmentation. The same RGB images of tomato leaves captured from real-world agricultural sites were used to train the two DNN models. Precision, recall, and F1-score were used to compare the performance of two DNN models. In terms of diagnosing the tomato leaf miner, the DNN model for segmentation outperformed the DNN model for classification, with higher precision, recall, and F1-score values. Furthermore, there were no false negative cases in the prediction of the DNN model for segmentation, indicating that it is adequate for detecting plant diseases and pests.
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30
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Munteanu D, Moina D, Zamfir CG, Petrea ȘM, Cristea DS, Munteanu N. Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models. SENSORS (BASEL, SWITZERLAND) 2022; 22:9536. [PMID: 36502238 PMCID: PMC9738404 DOI: 10.3390/s22239536] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.
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Affiliation(s)
- Dan Munteanu
- Faculty of Automation, Computer Sciences, Electronics and Electrical Engineering, “Dunǎrea de Jos” University of Galaţi, No. 111 Street Domneascǎ, 800210 Galati, Romania
| | - Diana Moina
- Faculty of Automation, Computer Sciences, Electronics and Electrical Engineering, “Dunǎrea de Jos” University of Galaţi, No. 111 Street Domneascǎ, 800210 Galati, Romania
| | - Cristina Gabriela Zamfir
- Faculty of Economics and Business Administration, “Dunarea de Jos” University of Galati, 800008 Galati, Romania
| | - Ștefan Mihai Petrea
- Faculty of Economics and Business Administration, “Dunarea de Jos” University of Galati, 800008 Galati, Romania
- Faculty of Food Science and Engineering, “Dunarea de Jos” University of Galati, 800201 Galati, Romania
| | - Dragos Sebastian Cristea
- Faculty of Economics and Business Administration, “Dunarea de Jos” University of Galati, 800008 Galati, Romania
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31
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Zhang G, Xu T, Tian Y. Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages. PLANT METHODS 2022; 18:123. [PMID: 36403061 PMCID: PMC9675130 DOI: 10.1186/s13007-022-00955-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Rice blast, which is prevalent worldwide, represents a serious threat to harvested crop yield and quality. Hyperspectral imaging, an emerging technology used in plant disease research, is a stable, repeatable method for disease grading. Current methods for assessing disease severity have mostly focused on individual growth stages rather than multiple ones. In this study, the spectral reflectance ratio (SRR) of whole leaves were calculated, the sensitive wave bands were selected using the successive projections algorithm (SPA) and the support vector machine (SVM) models were constructed to assess rice leaf blast severity over multiple growth stages. RESULTS The average accuracy, micro F1 values, and macro F1 values of the full-spectrum-based SVM model were respectively 94.75%, 0.869, and 0.883 in 2019; 92.92%, 0.823, and 0.808 in 2021; and 88.09%, 0.702, and 0.757 under the 2019-2021 combined model. The SRR-SVM model could be used to evaluate rice leaf blast disease during multiple growth stages and had good generalizability. CONCLUSIONS The proposed SRR data analysis method is able to eliminate differences among individuals to some extent, thus allowing for its application to assess rice leaf blast severity over multiple growth stages. Our approach, which can supplement single-stage disease-degree classification, provides a possible direction for future research on the assessment of plant disease severity during multiple growth stages.
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Affiliation(s)
| | - Tongyu Xu
- Shenyang Agricultural University, Shenyang, China
| | - Youwen Tian
- Shenyang Agricultural University, Shenyang, China
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Zhang G, Xu T, Tian Y, Feng S, Zhao D, Guo Z. Classification of rice leaf blast severity using hyperspectral imaging. Sci Rep 2022; 12:19757. [PMID: 36396749 PMCID: PMC9672119 DOI: 10.1038/s41598-022-22074-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/10/2022] [Indexed: 11/18/2022] Open
Abstract
Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.
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Affiliation(s)
- Guosheng Zhang
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
| | - Tongyu Xu
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
| | - Youwen Tian
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
| | - Shuai Feng
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
| | - Dongxue Zhao
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
| | - Zhonghui Guo
- grid.412557.00000 0000 9886 8131Shenyang Agricultural University, Shenyang, China
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33
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Chen R, Qi H, Liang Y, Yang M. Identification of plant leaf diseases by deep learning based on channel attention and channel pruning. FRONTIERS IN PLANT SCIENCE 2022; 13:1023515. [PMID: 36438120 PMCID: PMC9686387 DOI: 10.3389/fpls.2022.1023515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.
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Affiliation(s)
- Riyao Chen
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou, Guangdong, China
| | - Haixia Qi
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Yu Liang
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou, Guangdong, China
| | - Mingchao Yang
- College of Horticulture, South China Agricultural University, Guangzhou, China
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Rairdin A, Fotouhi F, Zhang J, Mueller DS, Ganapathysubramanian B, Singh AK, Dutta S, Sarkar S, Singh A. Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. FRONTIERS IN PLANT SCIENCE 2022; 13:966244. [PMID: 36340398 PMCID: PMC9634489 DOI: 10.3389/fpls.2022.966244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/26/2022] [Indexed: 06/07/2023]
Abstract
Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.
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Affiliation(s)
- Ashlyn Rairdin
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Fateme Fotouhi
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Jiaoping Zhang
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Daren S. Mueller
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States
| | | | - Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA, United States
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
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35
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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36
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Divyanth LG, Marzougui A, González-Bernal MJ, McGee RJ, Rubiales D, Sankaran S. Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea ( Pisum sativum L.). SENSORS (BASEL, SWITZERLAND) 2022; 22:7237. [PMID: 36236336 PMCID: PMC9572822 DOI: 10.3390/s22197237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.
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Affiliation(s)
- L. G. Divyanth
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
- Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
| | | | - Rebecca J. McGee
- Grain Legume Genetics and Physiology Research Unit, US Department of Agriculture-Agricultural Research Service (USDA-ARS), Pullman, WA 99164, USA
| | - Diego Rubiales
- The Institute for Sustainable Agriculture, Spanish National Research Council, 14001 Cordova, Spain
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
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37
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Turhal UC. Vegetation detection using vegetation indices algorithm supported by statistical machine learning. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:826. [PMID: 36152226 DOI: 10.1007/s10661-022-10425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method's performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN's and RF's performance.
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Affiliation(s)
- Umit Cigdem Turhal
- Engineering Faculty, Electric and Electronics Engineering Department, Bilecik Seyh Edebali University, Bilecik, Turkey, 11210.
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38
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Arjoune Y, Sugunaraj N, Peri S, Nair SV, Skurdal A, Ranganathan P, Johnson B. Soybean cyst nematode detection and management: a review. PLANT METHODS 2022; 18:110. [PMID: 36071455 PMCID: PMC9450454 DOI: 10.1186/s13007-022-00933-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Soybeans play a key role in global food security. U.S. soybean yields, which comprise [Formula: see text] of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods-adopted by most of the laboratories to make recommendations-is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN's increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.
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Affiliation(s)
- Youness Arjoune
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, USA
| | - Niroop Sugunaraj
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, USA
| | - Sai Peri
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, USA
| | - Sreejith V. Nair
- Department of Aviation, University of North Dakota, Grand Forks, USA
| | - Anton Skurdal
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, USA
| | - Prakash Ranganathan
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, USA
| | - Burton Johnson
- Plant Sciences, North Dakota State University, Fargo, USA
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Ye J, Song J, Gao Y, Lu X, Pei W, Li F, Feng H, Yang W. An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves. FRONTIERS IN PLANT SCIENCE 2022; 13:968855. [PMID: 36119566 PMCID: PMC9478445 DOI: 10.3389/fpls.2022.968855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Tobacco is one of the important economic crops all over the world. Tobacco mosaic virus (TMV) seriously affects the yield and quality of tobacco leaves. The expression of TMV in tobacco leaves can be analyzed by detecting green fluorescence-related traits after inoculation with the infectious clone of TMV-GFP (Tobacco mosaic virus - green fluorescent protein). However, traditional methods for detecting TMV-GFP are time-consuming and laborious, and mostly require a lot of manual procedures. In this study, we develop a low-cost machine-vision-based phenotyping platform for the automatic evaluation of fluorescence-related traits in tobacco leaf based on digital camera and image processing. A dynamic monitoring experiment lasting 7 days was conducted to evaluate the efficiency of this platform using Nicotiana tabacum L. with a total of 14 samples, including the wild-type strain SR1 and 4 mutant lines generated by RNA interference technology. As a result, we found that green fluorescence area and brightness generally showed an increasing trend over time, and the trends were different among these SR1 and 4 mutant lines samples, where the maximum and minimum of green fluorescence area and brightness were mutant-4 and mutant-1 respectively. In conclusion, the platform can full-automatically extract fluorescence-related traits with the advantage of low-cost and high accuracy, which could be used in detecting dynamic changes of TMV-GFP in tobacco leaves.
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Affiliation(s)
- Junli Ye
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuan Gao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xu Lu
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, College of Horticulture, Hainan University, Haikou, China
| | - Wenyue Pei
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Feng Li
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
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40
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Cao Y, Yuan P, Xu H, Martínez-Ortega JF, Feng J, Zhai Z. Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution. FRONTIERS IN PLANT SCIENCE 2022; 13:963170. [PMID: 35909723 PMCID: PMC9328758 DOI: 10.3389/fpls.2022.963170] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.
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Affiliation(s)
- Yifei Cao
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Peisen Yuan
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - José Fernán Martínez-Ortega
- Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Jiarui Feng
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Zhaoyu Zhai
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
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41
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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Danilevicz MF, Gill M, Anderson R, Batley J, Bennamoun M, Bayer PE, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Front Genet 2022; 13:822173. [PMID: 35664329 PMCID: PMC9159391 DOI: 10.3389/fgene.2022.822173] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
- *Correspondence: David Edwards,
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Mostafa S, Mondal D, Beck MA, Bidinosti CP, Henry CJ, Stavness I. Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification. Front Artif Intell 2022; 5:871162. [PMID: 35647528 PMCID: PMC9132261 DOI: 10.3389/frai.2022.871162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/15/2022] [Indexed: 12/02/2022] Open
Abstract
The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.
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Affiliation(s)
- Sakib Mostafa
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- *Correspondence: Sakib Mostafa
| | - Debajyoti Mondal
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Michael A. Beck
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
- Department of Applied Science, University of Winnipeg, Winnipeg, MB, Canada
| | | | | | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Qian X, Zhang C, Chen L, Li K. Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention. FRONTIERS IN PLANT SCIENCE 2022; 13:864486. [PMID: 35574079 PMCID: PMC9096888 DOI: 10.3389/fpls.2022.864486] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 06/15/2023]
Abstract
Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.
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Affiliation(s)
- Xiufeng Qian
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China
| | - Chengqi Zhang
- School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Li Chen
- School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China
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45
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Chiteri KO, Jubery TZ, Dutta S, Ganapathysubramanian B, Cannon S, Singh A. Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel. FRONTIERS IN PLANT SCIENCE 2022; 12:808001. [PMID: 35154202 PMCID: PMC8828542 DOI: 10.3389/fpls.2021.808001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.
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Affiliation(s)
- Kevin O. Chiteri
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Talukder Zaki Jubery
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA, United States
| | | | - Steven Cannon
- Department of Agronomy, Iowa State University, Ames, IA, United States
- USDA—Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
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Abstract
Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.
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Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery. REMOTE SENSING 2022. [DOI: 10.3390/rs14020396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.
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48
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Zhao D, Feng S, Cao Y, Yu F, Guan Q, Li J, Zhang G, Xu T. Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm. FRONTIERS IN PLANT SCIENCE 2022; 13:879668. [PMID: 35599890 PMCID: PMC9120945 DOI: 10.3389/fpls.2022.879668] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/19/2022] [Indexed: 05/03/2023]
Abstract
Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.
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Affiliation(s)
- Dongxue Zhao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Shuai Feng
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Yingli Cao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Fenghua Yu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Qiang Guan
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Jinpeng Li
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Guosheng Zhang
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Tongyu Xu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
- *Correspondence: Tongyu Xu,
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Zheng Z, Liu Y, He M, Chen D, Sun L, Zhu F. Effective band selection of hyperspectral image by an attention mechanism-based convolutional network. RSC Adv 2022; 12:8750-8759. [PMID: 35424797 PMCID: PMC8985171 DOI: 10.1039/d1ra07662k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/11/2022] [Indexed: 11/21/2022] Open
Abstract
An attention mechanism-based 3D-CNN network was proposed to select the effective bands of hyperspectral images while carrying out the model training.
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Affiliation(s)
- Zengwei Zheng
- School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China
- Intelligent Plant Factory of Zhejiang Province Engineering Lab, Hangzhou 310015, China
| | - Yi Liu
- College of Computer Science & Technology, Zhejiang University, Hangzhou 310027, China
| | - Mengzhu He
- School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China
- Intelligent Plant Factory of Zhejiang Province Engineering Lab, Hangzhou 310015, China
| | - Dan Chen
- School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China
- Intelligent Plant Factory of Zhejiang Province Engineering Lab, Hangzhou 310015, China
| | - Lin Sun
- School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China
- Intelligent Plant Factory of Zhejiang Province Engineering Lab, Hangzhou 310015, China
| | - Fengle Zhu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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50
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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