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Gomez D, Selvaraj MG, Casas J, Mathiyazhagan K, Rodriguez M, Assefa T, Mlaki A, Nyakunga G, Kato F, Mukankusi C, Girma E, Mosquera G, Arredondo V, Espitia E. Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Sci Rep 2024; 14:15596. [PMID: 38971939 PMCID: PMC11227504 DOI: 10.1038/s41598-024-66281-w] [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: 02/24/2024] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers' ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.
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Affiliation(s)
- Daniela Gomez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
| | - Michael Gomez Selvaraj
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia.
| | - Jorge Casas
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
| | - Kavino Mathiyazhagan
- Department of Horticulture, Agricultural College and Research Institute, Tamil Nadu Agriculture University, Vazhavachanur, Tiruvannamalai, Tamil Nadu, India
| | - Michael Rodriguez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
| | - Teshale Assefa
- International Center for Tropical Agriculture, Arusha, Tanzania
| | - Anna Mlaki
- International Center for Tropical Agriculture, Arusha, Tanzania
| | | | - Fred Kato
- International Center for Tropical Agriculture, Kawanda, Uganda
| | - Clare Mukankusi
- International Center for Tropical Agriculture, Kawanda, Uganda
| | - Ellena Girma
- International Center for Tropical Agriculture, Arusha, Tanzania
| | - Gloria Mosquera
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
| | - Victoria Arredondo
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
| | - Ernesto Espitia
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Cali, Colombia
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Isinkaye FO, Olusanya MO, Singh PK. Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review. Heliyon 2024; 10:e29583. [PMID: 38737274 PMCID: PMC11088271 DOI: 10.1016/j.heliyon.2024.e29583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 03/30/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024] Open
Abstract
The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.
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Affiliation(s)
- Folasade Olubusola Isinkaye
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Michael Olusoji Olusanya
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Pramod Kumar Singh
- Department of Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, 474015, MP, India
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Ye R, Shao G, He Y, Gao Q, Li T. YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea. SENSORS (BASEL, SWITZERLAND) 2024; 24:2896. [PMID: 38733002 PMCID: PMC11086262 DOI: 10.3390/s24092896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/28/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024]
Abstract
In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.
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Affiliation(s)
- Rong Ye
- College of Food Science and Technology, Yunnan Agricultural University, Kunming 650201, China;
- The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China;
| | - Guoqi Shao
- The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China;
| | - Yun He
- Big Data College, Yunnan Agricultural University, Kunming 650201, China; (Y.H.); (Q.G.)
| | - Quan Gao
- Big Data College, Yunnan Agricultural University, Kunming 650201, China; (Y.H.); (Q.G.)
| | - Tong Li
- The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China;
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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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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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6
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Nourinejhad Zarghani S, Monavari M, Nourinejhad Zarghani A, Nouri S, Ehlers J, Hamacher J, Bandte M, Büttner C. Quantifying Plant Viruses: Evolution from Bioassay to Infectivity Dilution Curves along the Model of Tobamoviruses. Viruses 2024; 16:440. [PMID: 38543805 PMCID: PMC10974926 DOI: 10.3390/v16030440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 05/23/2024] Open
Abstract
This review describes the development of the bioassay as a means of quantifying plant viruses, with particular attention to tobamovirus. It delves into various models used to establish a correlation between virus particle concentration and the number of induced local lesions (the infectivity dilution curve), including the Poisson, Furumoto and Mickey, Kleczkowski, Growth curve, and modified Poisson models. The parameters of each model are described, and their application or performance in the context of the tobacco mosaic virus is explored. This overview highlights the enduring value of the infectivity dilution curve in tobamovirus quantification, providing valuable insights for researchers or practitioners of bioassays and theoreticians of modeling.
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Affiliation(s)
- Shaheen Nourinejhad Zarghani
- Division Phytomedicine, Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität in Berlin, Lentzeallee 55–57, 14197 Berlin, Germany; (S.N.); (J.E.); (M.B.); (C.B.)
| | - Mehran Monavari
- Section eScience, Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany;
| | - Amin Nourinejhad Zarghani
- School of Mechanical Engineering, Hamburg University of Technology, Eissendorfer Str. 38, 21073 Hamburg, Germany;
| | - Sahar Nouri
- Division Phytomedicine, Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität in Berlin, Lentzeallee 55–57, 14197 Berlin, Germany; (S.N.); (J.E.); (M.B.); (C.B.)
| | - Jens Ehlers
- Division Phytomedicine, Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität in Berlin, Lentzeallee 55–57, 14197 Berlin, Germany; (S.N.); (J.E.); (M.B.); (C.B.)
- Menno Chemie Vertrieb GmbH, Langer Kamp 104, 22850 Norderstedt, Germany
| | - Joachim Hamacher
- Institute of Crop Science and Resource Conservation (INRES)—Plant Pathology, Universität Bonn, Nussallee 9, 53115 Bonn, Germany;
| | - Martina Bandte
- Division Phytomedicine, Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität in Berlin, Lentzeallee 55–57, 14197 Berlin, Germany; (S.N.); (J.E.); (M.B.); (C.B.)
| | - Carmen Büttner
- Division Phytomedicine, Faculty of Life Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität in Berlin, Lentzeallee 55–57, 14197 Berlin, Germany; (S.N.); (J.E.); (M.B.); (C.B.)
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7
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Wu P, Zhang Z, Peng X, Wang R. Deep learning solutions for smart city challenges in urban development. Sci Rep 2024; 14:5176. [PMID: 38431741 PMCID: PMC11319439 DOI: 10.1038/s41598-024-55928-3] [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: 11/22/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024] Open
Abstract
In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.
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Affiliation(s)
- Pengjun Wu
- School of Plastic Arts, Daegu University, Gyeongsan, Gyeongsangbukdo, 38453, South Korea.
| | - Zhanzhi Zhang
- College of Art and Design, Southwest Forestry University, Kunming, 650224, Yunnan, China
| | - Xueyi Peng
- Sichuan Energy Construction Group Design and Research Institute, Chengdu, 610011, Sichuan, China
| | - Ran Wang
- China Construction Eighth Engineering Division Corp, LTD, Wuhan, 430000, Hubei, China
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Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L. Revolutionizing crop disease detection with computational deep learning: a comprehensive review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:302. [PMID: 38401024 PMCID: PMC10894121 DOI: 10.1007/s10661-024-12454-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.
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Affiliation(s)
- Habiba N Ngugi
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg, KwaZulu-Natal, 3201, South Africa
| | - Absalom E Ezugwu
- Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa.
| | - Andronicus A Akinyelu
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa
| | - Laith Abualigah
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia.
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan.
- MEU Research Unit, Middle East University, Amman, 11831, Jordan.
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon.
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia.
- Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan.
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9
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Meraj T, Sharif MI, Raza M, Alabrah A, Kadry S, Gandomi AH. Computer vision-based plants phenotyping: A comprehensive survey. iScience 2024; 27:108709. [PMID: 38269095 PMCID: PMC10805646 DOI: 10.1016/j.isci.2023.108709] [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] [Indexed: 01/26/2024] Open
Abstract
The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.
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Affiliation(s)
- Talha Meraj
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
| | - Amerah Alabrah
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Amir H. Gandomi
- Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
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10
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Kini AS, Prema KV, Pai SN. Early stage black pepper leaf disease prediction based on transfer learning using ConvNets. Sci Rep 2024; 14:1404. [PMID: 38228767 PMCID: PMC10791634 DOI: 10.1038/s41598-024-51884-0] [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: 08/17/2023] [Accepted: 01/10/2024] [Indexed: 01/18/2024] Open
Abstract
Plants get exposed to diseases, insects and fungus. This causes heavy damages to crop resulting in various leaves diseases. Leaf diseases can be diagnosed at an early stage with the aid of a smart computer vision system and timely disease prevention can be targeted. Black pepper is a medicinal plant that is extensively used in Ayurvedic medicine because of its therapeutic properties. The proposed work represents an intelligent transfer learning technique through state-of-the-art deep learning implementation using convolutional neural network to predict the presence of prominent diseases in black pepper leaves. The ImageNet dataset available online is used for training deep neural network. Later, this trained network is utilized for the prediction of the newly developed black pepper leaf image dataset. The developed data set consist of real time leaf images, which are candidly taken from the fields and annotated under supervision of an expert. The leaf diseases considered are anthracnose, slow wilt, early stage phytophthora, phytophthora and yellowing. The hyperparameters chosen for tuning in to deep learning models are initial learning rates, optimization algorithm, image batches, epochs, validation and training data, etc. The accuracy obtained with 0.001 learning rate ranges from 99.1 to 99.7% for the Inception V3, GoogleNet, SqueezeNet and Resnet18 models. Proposed Resnet18 model outperforms all model with 99.67% accuracy. The resulting validation accuracy obtained using these models is high and the validation loss is low. This work represents improvement in agriculture and a cutting edge deep neural network method for early stage leaf disease identification and prediction. This is an approach using a deep learning network to predict early stage black pepper leaf diseases.
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Affiliation(s)
- Anita S Kini
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - K V Prema
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education (MAHE), Manipal, India.
| | - Smitha N Pai
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India
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11
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Khan AT, Jensen SM, Khan AR, Li S. Plant disease detection model for edge computing devices. FRONTIERS IN PLANT SCIENCE 2023; 14:1308528. [PMID: 38143571 PMCID: PMC10748432 DOI: 10.3389/fpls.2023.1308528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023]
Abstract
In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.
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Affiliation(s)
- Ameer Tamoor Khan
- Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark
| | - Signe Marie Jensen
- Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rehman Khan
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
| | - Shuai Li
- Deparment of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
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12
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Quiñones R, Samal A, Das Choudhury S, Muñoz-Arriola F. OSC-CO 2: coattention and cosegmentation framework for plant state change with multiple features. FRONTIERS IN PLANT SCIENCE 2023; 14:1211409. [PMID: 38023863 PMCID: PMC10644038 DOI: 10.3389/fpls.2023.1211409] [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: 04/24/2023] [Accepted: 10/06/2023] [Indexed: 12/01/2023]
Abstract
Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.
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Affiliation(s)
- Rubi Quiñones
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
- Computer Science Department, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Ashok Samal
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Sruti Das Choudhury
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Francisco Muñoz-Arriola
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
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13
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Cheng H, Li H. Identification of apple leaf disease via novel attention mechanism based convolutional neural network. FRONTIERS IN PLANT SCIENCE 2023; 14:1274231. [PMID: 37920720 PMCID: PMC10619150 DOI: 10.3389/fpls.2023.1274231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/19/2023] [Indexed: 11/04/2023]
Abstract
Introduction The identification of apple leaf diseases is crucial for apple production. Methods To assist farmers in promptly recognizing leaf diseases in apple trees, we propose a novel attention mechanism. Building upon this mechanism and MobileNet v3, we introduce a new deep learning network. Results and discussion Applying this network to our carefully curated dataset, we achieved an impressive accuracy of 98.7% in identifying apple leaf diseases, surpassing similar models such as EfficientNet-B0, ResNet-34, and DenseNet-121. Furthermore, the precision, recall, and f1-score of our model also outperform these models, while maintaining the advantages of fewer parameters and less computational consumption of the MobileNet network. Therefore, our model has the potential in other similar application scenarios and has broad prospects.
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Affiliation(s)
| | - Heming Li
- School of Intelligence Engineering, Shandong Management University, Jinan, China
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14
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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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15
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Liu J, Wang X. Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features. FRONTIERS IN PLANT SCIENCE 2023; 14:1255119. [PMID: 37877077 PMCID: PMC10590886 DOI: 10.3389/fpls.2023.1255119] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
To address the challenges of insufficient accuracy in detecting tomato disease object detection caused by dense target distributions, large-scale variations, and poor feature information of small objects in complex backgrounds, this study proposes the tomato disease object detection method that integrates prior knowledge attention mechanism and multi-scale features (PKAMMF). Firstly, the visual features of tomato disease images are fused with prior knowledge through the prior knowledge attention mechanism to obtain enhanced visual features corresponding to tomato diseases. Secondly, a new feature fusion layer is constructed in the Neck section to reduce feature loss. Furthermore, a specialized prediction layer specifically designed to improve the model's ability to detect small targets is incorporated. Finally, a new loss function known as A-SIOU (Adaptive Structured IoU) is employed to optimize the performance of the model in terms of bounding box regression. The experimental results on the self-built tomato disease dataset demonstrate the effectiveness of the proposed approach, and it achieves a mean average precision (mAP) of 91.96%, which is a 3.86% improvement compared to baseline methods. The results show significant improvements in the detection performance of multi-scale tomato disease objects.
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Affiliation(s)
- Jun Liu
- Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China
| | - Xuewei Wang
- Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China
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16
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Shi Y, Han L, González-Moreno P, Dancey D, Huang W, Zhang Z, Liu Y, Huang M, Miao H, Dai M. A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data. FRONTIERS IN PLANT SCIENCE 2023; 14:1250844. [PMID: 37860254 PMCID: PMC10582577 DOI: 10.3389/fpls.2023.1250844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/13/2023] [Indexed: 10/21/2023]
Abstract
Introduction Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. Methods In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. Results and discussion The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization.
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Affiliation(s)
- Yue Shi
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
| | - Liangxiu Han
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | | | - Darren Dancey
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Wenjiang Huang
- Aerospace Information research Institute, Chinese Academy of Sciences (CAS), Beijing, China
| | - Zhiqiang Zhang
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
| | - Yuanyuan Liu
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Mengning Huang
- School of Computing, Beijing University of Technology, Beijing, China
| | - Hong Miao
- School of Mechanical Engineering, Yangzhou University, Yangzhou, China
| | - Min Dai
- School of Mechanical Engineering, Yangzhou University, Yangzhou, China
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17
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Chen Y, Zhou B, Ye D, Cui L, Feng L, Han X. An Optimization Method of Deep Transfer Learning for Vegetation Segmentation under Rainy and Dry Season Differences in a Dry Thermal Valley. PLANTS (BASEL, SWITZERLAND) 2023; 12:3383. [PMID: 37836123 PMCID: PMC10574146 DOI: 10.3390/plants12193383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
Deep learning networks might require re-training for different datasets, consuming significant manual labeling and training time. Transfer learning uses little new data and training time to enable pre-trained network segmentation in relevant scenarios (e.g., different vegetation images in rainy and dry seasons); however, existing transfer learning methods lack systematicity and controllability. So, an MTPI method (Maximum Transfer Potential Index method) was proposed to find the optimal conditions in data and feature quantity for transfer learning (MTPI conditions) in this study. The four pre-trained deep networks (Seg-Net (Semantic Segmentation Networks), FCN (Fully Convolutional Networks), Mobile net v2, and Res-Net 50 (Residual Network)) using the rainy season dataset showed that Res-Net 50 had the best accuracy with 93.58% and an WIoU (weight Intersection over Union) of 88.14%, most worthy to transfer training in vegetation segmentation. By obtaining each layer's TPI performance (Transfer Potential Index) of the pre-trained Res-Net 50, the MTPI method results show that the 1000-TDS and 37-TP were estimated as the best training speed with the smallest dataset and a small error risk. The MTPI transfer learning results show 91.56% accuracy and 84.86% WIoU with 90% new dataset reduction and 90% iteration reduction, which is informative for deep networks in segmentation tasks between complex vegetation scenes.
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Affiliation(s)
- Yayong Chen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350012, China;
- Fujian Key Laboratory of Agricultural Information Sensoring Technology, Fuzhou 350012, China
| | - Beibei Zhou
- State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350012, China;
- Fujian Key Laboratory of Agricultural Information Sensoring Technology, Fuzhou 350012, China
| | - Lei Cui
- China Renewable Energy Engineering Institute, Beijing 100032, China;
| | - Lei Feng
- Central South Survey and Design Institute Group Co., Ltd., Changsha 410014, China;
| | - Xiaojie Han
- China Electric Construction Group Beijing Survey and Design Institute Co., Beijing 100024, China;
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18
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Xu M, Kim H, Yang J, Fuentes A, Meng Y, Yoon S, Kim T, Park DS. Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1225409. [PMID: 37810377 PMCID: PMC10557492 DOI: 10.3389/fpls.2023.1225409] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023]
Abstract
Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.
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Affiliation(s)
- Mingle Xu
- Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Hyongsuk Kim
- Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Jucheng Yang
- College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China
| | - Alvaro Fuentes
- Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Yao Meng
- Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Sook Yoon
- Department of Computer Engineering, Mokpo National University, Muan, Republic of Korea
| | - Taehyun Kim
- National Institute of Agricultural Sciences, Wanju, Republic of Korea
| | - Dong Sun Park
- Department of Electronic Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
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19
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Song Z, Wang D, Xiao L, Zhu Y, Cao G, Wang Y. DaylilyNet: A Multi-Task Learning Method for Daylily Leaf Disease Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:7879. [PMID: 37765935 PMCID: PMC10537663 DOI: 10.3390/s23187879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023]
Abstract
Timely detection and management of daylily diseases are crucial to prevent yield reduction. However, detection models often struggle with handling the interference of complex backgrounds, leading to low accuracy, especially in detecting small targets. To address this problem, we propose DaylilyNet, an object detection algorithm that uses multi-task learning to optimize the detection process. By incorporating a semantic segmentation loss function, the model focuses its attention on diseased leaf regions, while a spatial global feature extractor enhances interactions between leaf and background areas. Additionally, a feature alignment module improves localization accuracy by mitigating feature misalignment. To investigate the impact of information loss on model detection performance, we created two datasets. One dataset, referred to as the 'sliding window dataset', was obtained by splitting the original-resolution images using a sliding window. The other dataset, known as the 'non-sliding window dataset', was obtained by downsampling the images. Experimental results in the 'sliding window dataset' and the 'non-sliding window dataset' demonstrate that DaylilyNet outperforms YOLOv5-L in mAP@0.5 by 5.2% and 4.0%, while reducing parameters and time cost. Compared to other models, our model maintains an advantage even in scenarios where there is missing information in the training dataset.
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Affiliation(s)
- Zishen Song
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
| | - Dong Wang
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
| | - Lizhong Xiao
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
| | - Yongjian Zhu
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
- Ningbo Minjie Information Technology Co., Ltd., Ningbo 315040, China
| | - Guogang Cao
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
| | - Yuli Wang
- Shanghai Institute of Technology, College of Computer Science and Information Engineering, Shanghai 201418, China
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20
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Ma R, Fuentes A, Yoon S, Lee WY, Kim SC, Kim H, Park DS. Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds. FRONTIERS IN PLANT SCIENCE 2023; 14:1211075. [PMID: 37711291 PMCID: PMC10499048 DOI: 10.3389/fpls.2023.1211075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/08/2023] [Indexed: 09/16/2023]
Abstract
Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system's accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system's ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity.
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Affiliation(s)
- Ruihan Ma
- Department of Electronics Engineering, Jeonbuk National University, Jeonbuk, Republic of Korea
- Core Research Institute for Intelligent Robots, Jeonbuk National University, Jeonbuk, Republic of Korea
| | - Alvaro Fuentes
- Department of Electronics Engineering, Jeonbuk National University, Jeonbuk, Republic of Korea
- Core Research Institute for Intelligent Robots, Jeonbuk National University, Jeonbuk, Republic of Korea
| | - Sook Yoon
- Department of Computer Engineering, Mokpo National University, Jeonnam, Republic of Korea
| | - Woon Yong Lee
- Department of Food Engineering Research, Intelligent Robot Studio Co. Ltd., Gyeonggi-do, Republic of Korea
| | - Sang Cheol Kim
- Core Research Institute for Intelligent Robots, Jeonbuk National University, Jeonbuk, Republic of Korea
| | - Hyongsuk Kim
- Department of Electronics Engineering, Jeonbuk National University, Jeonbuk, Republic of Korea
- Core Research Institute for Intelligent Robots, Jeonbuk National University, Jeonbuk, Republic of Korea
| | - Dong Sun Park
- Department of Electronics Engineering, Jeonbuk National University, Jeonbuk, Republic of Korea
- Core Research Institute for Intelligent Robots, Jeonbuk National University, Jeonbuk, Republic of Korea
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21
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Wang X, Cao W. GACN: Generative Adversarial Classified Network for Balancing Plant Disease Dataset and Plant Disease Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:6844. [PMID: 37571626 PMCID: PMC10422207 DOI: 10.3390/s23156844] [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: 06/28/2023] [Revised: 07/21/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023]
Abstract
Plant diseases are a critical threat to the agricultural sector. Therefore, accurate plant disease classification is important. In recent years, some researchers have used synthetic images of GAN to enhance plant disease recognition accuracy. In this paper, we propose a generative adversarial classified network (GACN) to further improve plant disease recognition accuracy. The GACN comprises a generator, discriminator, and classifier. The proposed model can not only enhance convolutional neural network performance by generating synthetic images to balance plant disease datasets but the GACN classifier can also be directly applied to plant disease recognition tasks. Experimental results on the PlantVillage and AI Challenger 2018 datasets show that the contribution of the proposed method to improve the discriminability of the convolution neural network is greater than that of the label-conditional methods of CGAN, ACGAN, BAGAN, and MFC-GAN. The accuracy of the trained classifier for plant disease recognition is also better than that of the plant disease recognition models studied on public plant disease datasets. In addition, we conducted several experiments to observe the effects of different numbers and resolutions of synthetic images on the discriminability of convolutional neural network.
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Affiliation(s)
- Xiaotian Wang
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Weiqun Cao
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
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22
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Guan H, Fu C, Zhang G, Li K, Wang P, Zhu Z. A lightweight model for efficient identification of plant diseases and pests based on deep learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1227011. [PMID: 37521914 PMCID: PMC10382237 DOI: 10.3389/fpls.2023.1227011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023]
Abstract
Plant diseases and pests have always been major contributors to losses that occur in agriculture. Currently, the use of deep learning-based convolutional neural network models allows for the accurate identification of different types of plant diseases and pests. To enable more efficient identification of plant diseases and pests, we design a novel network architecture called Dise-Efficient based on the EfficientNetV2 model. Our experiments demonstrate that training this model using a dynamic learning rate decay strategy can improve the accuracy of plant disease and pest identification. Furthermore, to improve the model's generalization ability, transfer learning is incorporated into the training process. Experimental results indicate that the Dise-Efficient model boasts a compact size of 13.3 MB. After being trained using the dynamic learning rate decay strategy, the model achieves an accuracy of 99.80% on the Plant Village plant disease and pest dataset. Moreover, through transfer learning on the IP102 dataset, which represents real-world environmental conditions, the Dise-Efficient model achieves a recognition accuracy of 64.40% for plant disease and pest identification. In light of these results, the proposed Dise-Efficient model holds great potential as a valuable reference for the deployment of automatic plant disease and pest identification applications on mobile and embedded devices in the future.
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23
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David E, Ogidi F, Smith D, Chapman S, de Solan B, Guo W, Baret F, Stavness I. Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0059. [PMID: 38239739 PMCID: PMC10795497 DOI: 10.34133/plantphenomics.0059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 06/01/2023] [Indexed: 01/22/2024]
Abstract
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.
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Affiliation(s)
- Etienne David
- UMR 1114 EMMAH, INRAE, Avignon, France
- Arvalis – Institut du Végétal, Paris, France
| | - Franklin Ogidi
- Department of Computer Science,
University of Saskatchewan, Saskatoon, Canada
| | - Daniel Smith
- School of Food and Agricultural Sciences,
University of Queensland, Brisbane, Australia
| | - Scott Chapman
- School of Food and Agricultural Sciences,
University of Queensland, Brisbane, Australia
| | | | - Wei Guo
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
| | | | - Ian Stavness
- Department of Computer Science,
University of Saskatchewan, Saskatoon, Canada
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24
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Liu K, Qiu T, Yu Y, Li S, Li X. Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration. SENSORS (BASEL, SWITZERLAND) 2023; 23:5789. [PMID: 37447638 DOI: 10.3390/s23135789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 07/15/2023]
Abstract
Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fβw, and MAE).
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Affiliation(s)
- Kangwei Liu
- Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
| | - Tianchi Qiu
- Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
| | - Yinfeng Yu
- Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
| | - Songlin Li
- Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
| | - Xiuhong Li
- Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
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25
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Mittal U, Wadhawan A, Singla J, Jhanjhi NZ, Ghoniem RM, Ray SK, Abdelmaboud A. Plant Disease Detection and Classification: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4769. [PMID: 37430683 DOI: 10.3390/s23104769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/06/2023] [Accepted: 05/10/2023] [Indexed: 07/12/2023]
Abstract
A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
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Affiliation(s)
- Usha Mittal
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Ankita Wadhawan
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Jimmy Singla
- School of Engineering and Technology, CT University, Ludhiana 142024, Punjab, India
| | - N Z Jhanjhi
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Rania M Ghoniem
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Riyadh, Saudi Arabia
| | - Sayan Kumar Ray
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Abdelzahir Abdelmaboud
- Department of Information Systems, King Khalid University, Abha 61913, Muhayel Aseer, Saudi Arabia
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26
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Jia W, Xu Y, Lu Y, Yin X, Pan N, Jiang R, Ge X. An accurate green fruits detection method based on optimized YOLOX-m. FRONTIERS IN PLANT SCIENCE 2023; 14:1187734. [PMID: 37223802 PMCID: PMC10200941 DOI: 10.3389/fpls.2023.1187734] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 05/25/2023]
Abstract
Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network's ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables.
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Affiliation(s)
- Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Ying Xu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuqi Lu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Xiang Yin
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong, China
| | - Ningning Pan
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Ru Jiang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
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27
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Soeb MJA, Jubayer MF, Tarin TA, Al Mamun MR, Ruhad FM, Parven A, Mubarak NM, Karri SL, Meftaul IM. Tea leaf disease detection and identification based on YOLOv7 (YOLO-T). Sci Rep 2023; 13:6078. [PMID: 37055480 PMCID: PMC10102080 DOI: 10.1038/s41598-023-33270-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/11/2023] [Indexed: 04/15/2023] Open
Abstract
A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.
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Affiliation(s)
- Md Janibul Alam Soeb
- Department of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
| | - Md Fahad Jubayer
- Department of Food Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
| | - Tahmina Akanjee Tarin
- Department of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, Bangladesh
| | - Muhammad Rashed Al Mamun
- Department of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, Bangladesh
| | - Fahim Mahafuz Ruhad
- Department of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet, 3100, Bangladesh
| | - Aney Parven
- Global Centre for Environmental Remediation (GCER), College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Department of Agricultural Chemistry, Sher-e-Bangla Agricultural University, Dhaka, 1207, Bangladesh
| | - Nabisab Mujawar Mubarak
- Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan, BE1410, Brunei Darussalam.
| | - Soni Lanka Karri
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Bandar Seri Begawan, BE1410, Brunei Darussalam
| | - Islam Md Meftaul
- Global Centre for Environmental Remediation (GCER), College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia.
- Department of Agricultural Chemistry, Sher-e-Bangla Agricultural University, Dhaka, 1207, Bangladesh.
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28
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Shahoveisi F, Taheri Gorji H, Shahabi S, Hosseinirad S, Markell S, Vasefi F. Application of image processing and transfer learning for the detection of rust disease. Sci Rep 2023; 13:5133. [PMID: 36991013 PMCID: PMC10060580 DOI: 10.1038/s41598-023-31942-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying.
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Affiliation(s)
- Fereshteh Shahoveisi
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA.
- Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA.
| | - Hamed Taheri Gorji
- Biomedical Engineering Program, College of Engineering and Mine, University of North Dakota, Grand Forks, ND, USA
- SafetySpect Inc., 10100 Santa Monica Blvd., Suite 300, Los Angeles, CA, USA
| | - Seyedmojtaba Shahabi
- School of Electrical Engineering and Computer Science, College of Engineering and Mine, University of North Dakota, Grand Forks, ND, USA
| | - Seyedali Hosseinirad
- Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Samuel Markell
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA
| | - Fartash Vasefi
- SafetySpect Inc., 10100 Santa Monica Blvd., Suite 300, Los Angeles, CA, USA
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29
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Zhang S, Xie M. Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot's Picking Clustered Fruits of Chilies. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073408. [PMID: 37050468 PMCID: PMC10099055 DOI: 10.3390/s23073408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/08/2023] [Accepted: 03/15/2023] [Indexed: 06/12/2023]
Abstract
Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate the chilies that are blocked by branches and leaves, or other chilies. However, little is known about the recognition algorithms considering this situation. Failure to solve this problem will mean that the robot cannot accurately locate and collect chilies, which may even damage the picking robot's mechanical arm and end effector. Additionally, most of the existing ground target recognition algorithms are relatively complex, and there are many problems, such as numerous parameters and calculations. Many of the existing models have high requirements for hardware and poor portability. It is very difficult to perform these algorithms if the picking robots have limited computing and battery power. In view of these practical issues, we propose a target recognition-location scheme GNPD-YOLOv5s based on improved YOLOv5s in order to automatically identify the occluded and non-occluded chilies. Firstly, the lightweight optimization for Ghost module is introduced into our scheme. Secondly, pruning and distilling the model is designed to further reduce the number of parameters. Finally, the experimental data show that compared with the YOLOv5s model, the floating point operation number of the GNPD-YOLOv5s scheme is reduced by 40.9%, the model size is reduced by 46.6%, and the reasoning speed is accelerated from 29 ms/frame to 14 ms/frame. At the same time, the Mean Accuracy Precision (MAP) is reduced by 1.3%. Our model implements a lightweight network model and target recognition in the dense environment at a small cost. In our locating experiments, the maximum depth locating chili error is 1.84 mm, which meets the needs of a chili picking robot for chili recognition.
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30
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Shoaib M, Shah B, EI-Sappagh S, Ali A, Ullah A, Alenezi F, Gechev T, Hussain T, Ali F. An advanced deep learning models-based plant disease detection: A review of recent research. FRONTIERS IN PLANT SCIENCE 2023; 14:1158933. [PMID: 37025141 PMCID: PMC10070872 DOI: 10.3389/fpls.2023.1158933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 02/27/2023] [Indexed: 05/14/2023]
Abstract
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
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Affiliation(s)
- Muhammad Shoaib
- Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
- Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan
| | - Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Shaker EI-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
| | - Akhtar Ali
- Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Asad Ullah
- Department of Computer Science and Information Technology, Sarhad University of Science and Information Technology, Peshawar, Pakistan
| | - Fayadh Alenezi
- Department of Electrical Engineering, College of Engineering, Jouf University, Jouf, Saudi Arabia
| | - Tsanko Gechev
- Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, Plovdiv, Bulgaria
| | - Tariq Hussain
- School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China
| | - Farman Ali
- Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
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31
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Obasekore H, Fanni M, Ahmed SM, Parque V, Kang BY. Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm ( Spodoptera frugiperda). SENSORS (BASEL, SWITZERLAND) 2023; 23:3147. [PMID: 36991858 PMCID: PMC10056802 DOI: 10.3390/s23063147] [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: 01/26/2023] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot's smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.
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Affiliation(s)
- Hammed Obasekore
- Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Mohamed Fanni
- Production Engineering and Mechanical Design Department, Mansoura University, Mansoura 35516, Egypt
- Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
| | - Sabah Mohamed Ahmed
- Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
- Electrical Engineering Department, Assuit University, Assuit 71515, Egypt
| | - Victor Parque
- Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
- Department of Modern Mechanical Engineering, Waseda University, Tokyo 169-8050, Japan
| | - Bo-Yeong Kang
- Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
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32
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Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2023. [DOI: 10.1155/2023/5051005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for this method. Digital image processing can be used to monitor plant disease. Deep learning has made remarkable improvements in digital image processing in recent years, surpassing the older techniques. This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. The CNN’s backbone comprises AlexNet, ResNet, VGG-16, and DenseNet. The Adam and RmsProp optimization methods examine these networks’ relative performance, demonstrating that the DenseNet model with the RmsProp optimization approach achieves the most significant outcomes with the best accuracy of 99.9%.
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33
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Xiang L, Wen H, Zhao M. Pill Box Text Identification Using DBNet-CRNN. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3881. [PMID: 36900892 PMCID: PMC10002137 DOI: 10.3390/ijerph20053881] [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: 01/12/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
The recognition process of natural scenes is complicated at present, and images themselves may be complex owing to the special features of natural scenes. In this study, we use the detection and recognition of pill box text as an application scenario and design a deep-learning-based text detection algorithm for such natural scenes. We propose an end-to-end graphical text detection and recognition model and implement a detection system based on the B/S research application for pill box recognition, which uses DBNet as the text detection framework and a convolutional recurrent neural network (CRNN) as the text recognition framework. No prior image preprocessing is required in the detection and recognition processes. The recognition result from the back-end is returned to the front-end display. Compared with traditional methods, this recognition process reduces the complexity of preprocessing prior to image detection and improves the simplicity of the model application. Experiments on the detection and recognition of 100 pill boxes demonstrate that the proposed method achieves better accuracy in text localization and recognition results than the previous CTPN + CRNN method. The proposed method is significantly more accurate and easier to use than the traditional approach in terms of both training and recognition processes.
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34
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Phan QH, Nguyen VT, Lien CH, Duong TP, Hou MTK, Le NB. Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12040790. [PMID: 36840138 PMCID: PMC9959894 DOI: 10.3390/plants12040790] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 06/12/2023]
Abstract
Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.
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Affiliation(s)
- Quoc-Hung Phan
- Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
| | - Van-Tung Nguyen
- Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
| | - Chi-Hsiang Lien
- Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
| | - The-Phong Duong
- Department of Mechanical Engineering, HCMC University of Technology and Education, Ho Chi Minh City 700000, Vietnam
| | - Max Ti-Kuang Hou
- Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
| | - Ngoc-Bich Le
- School of Biomedical Engineering, International University, Ho Chi Minh City 700000, Vietnam
- Vietnam National University HCMC, Ho Chi Minh City 700000, Vietnam
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35
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Subhashini P, De Britto C J, Babu KU, Sumathy G. Artificial intelligence for the identification of healthy fruits and vegetables using MMDL-ABO. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2166592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- P. Subhashini
- Department of IT, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
| | | | - K. Upendra Babu
- Department of CSE, Bharath Institute of Higher Education and Research, Chennai, India
| | - G. Sumathy
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India
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36
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Apple leaf disease identification via improved CycleGAN and convolutional neural network. Soft comput 2023. [DOI: 10.1007/s00500-023-07811-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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37
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Meng X, Tian X, Wu Q, Chen Y, Pan J, Hang Y. Meta-baseline based on deep neuro-fuzzy network for few-shot plant leaf fungal diseases recognition. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2164402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Xiangyan Meng
- College of Arts and Sciences, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Xiaole Tian
- College of Engineering, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Qiufeng Wu
- College of Arts and Sciences, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Yiping Chen
- College of Engineering, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Jinchao Pan
- College of Engineering, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Yan Hang
- College of Engineering, Northeast Agricultural University, Harbin, People’s Republic of China
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38
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Dong X, Wang Q, Huang Q, Ge Q, Zhao K, Wu X, Wu X, Lei L, Hao G. PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0054. [PMID: 37213546 PMCID: PMC10194370 DOI: 10.34133/plantphenomics.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/23/2023]
Abstract
Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.
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Affiliation(s)
- Xinyu Dong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Qi Wang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
- Address correspondence to: (Q.W.); (G.H.)
| | - Qianding Huang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Qinglong Ge
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Kejun Zhao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Xingcai Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Xue Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Liang Lei
- The School of Physics and Optoelectronic Engineering,
Guangdong University of Technology, Guangzhou 510006, China
| | - Gefei Hao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education,
Guizhou University, Guiyang 550025, China
- Address correspondence to: (Q.W.); (G.H.)
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39
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Kaya Y, Gürsoy E. A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.101998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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40
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Mzoughi O, Yahiaoui I. Deep learning-based segmentation for disease identification. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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41
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Zhu R, Zou H, Li Z, Ni R. Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases. PLANTS (BASEL, SWITZERLAND) 2022; 12:plants12010169. [PMID: 36616300 PMCID: PMC9824080 DOI: 10.3390/plants12010169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 05/27/2023]
Abstract
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.
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42
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Kim D, Jeong S, Kim B, Kim SJ, Kim H, Jeong S, Yun GY, Kim KY, Park K. Automated Detection of Rice Bakanae Disease via Drone Imagery. SENSORS (BASEL, SWITZERLAND) 2022; 23:32. [PMID: 36616630 PMCID: PMC9824615 DOI: 10.3390/s23010032] [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: 10/26/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a system for the forecasting and automated inspection of rice Bakanae disease (RBD) infection rates via drone imagery. The proposed system synthesizes camera calibrations and area calculations in the optimal data domain to detect infected bunches and classify infected rice culm numbers. Optimal heights and angles for identification were examined via linear discriminant analysis and gradient magnitude by targeting the morphological features of RBD in drone imagery. Camera calibration and area calculation enabled distortion correction and simultaneous calculation of image area using a perspective transform matrix. For infection detection, a two-step configuration was used to recognize the infected culms through deep learning classifiers. The YOLOv3 and RestNETV2 101 models were used for detection of infected bunches and classification of the infected culm numbers, respectively. Accordingly, 3 m drone height and 0° angle to the ground were found to be optimal, yielding an infected bunches detection rate with a mean average precision of 90.49. The classification of number of infected culms in the infected bunch matched with an 80.36% accuracy. The RBD detection system that we propose can be used to minimize confusion and inefficiency during rice field inspection.
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Affiliation(s)
- Donghoon Kim
- IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea
| | - Sunghwan Jeong
- IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea
| | - Byoungjun Kim
- IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea
| | - Seo-jeong Kim
- IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea
| | - Heegon Kim
- Horticultural Research Institute, Jeonnam Agricultural Research & Extension Services, Iksan 58213, Republic of Korea
| | - Sooho Jeong
- Horticultural Research Institute, Jeonnam Agricultural Research & Extension Services, Iksan 58213, Republic of Korea
| | - Ga-yun Yun
- Horticultural Research Institute, Jeonnam Agricultural Research & Extension Services, Iksan 58213, Republic of Korea
| | - Kee-Yeun Kim
- Ministry of Agriculture, Food and Rural Affairs, 94 Dasom 2-ro, Government Complex-Sejong, Sejong-si 30110, Republic of Korea
| | - Keunho Park
- Ministry of Agriculture, Food and Rural Affairs, 94 Dasom 2-ro, Government Complex-Sejong, Sejong-si 30110, Republic of Korea
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43
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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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44
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Rice plant disease classification using dilated convolutional neural network with global average pooling. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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45
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Wen C, Chen H, Ma Z, Zhang T, Yang C, Su H, Chen H. Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting. FRONTIERS IN PLANT SCIENCE 2022; 13:973985. [PMID: 36570910 PMCID: PMC9783619 DOI: 10.3389/fpls.2022.973985] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/27/2022] [Indexed: 06/17/2023]
Abstract
Frequent outbreaks of agricultural pests can reduce crop production severely and restrict agricultural production. Therefore, automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. In recent years, pest recognition and detection have been rapidly improved with the development of deep learning-based methods. Although certain progress has been made in the research on pest detection and identification technology based on deep learning, there are still many problems in the production application in a field environment. This work presents a pest detector for multi-category dense and tiny pests named the Pest-YOLO. First, the idea of focal loss is introduced into the loss function using weight distribution to improve the attention of hard samples. In this way, the problems of hard samples arose from the uneven distribution of pest populations in a dataset and low discrimination features of small pests are relieved. Next, a non-Intersection over Union bounding box selection and suppression algorithm, the confluence strategy, is used. The confluence strategy can eliminate the errors and omissions of pest detection caused by occlusion, adhesion and unlabeling among tiny dense pest individuals to the greatest extent. The proposed Pest-YOLO model is verified on a large-scale pest image dataset, the Pest24, which includes more than 20k images with over 190k pests labeled by agricultural experts and categorized into 24 classes. Experimental results show that the Pest-YOLO can obtain 69.59% for mAP and 77.71% for mRecall on the 24-class pest dataset, which is 5.32% and 28.12% higher than the benchmark model YOLOv4. Meanwhile, our proposed model is superior to other several state-of-the-art methods, including the SSD, RetinaNet, Faster RCNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOX, DETR, TOOD, YOLOv3-W, and AF-RCNN detectors. The code of the proposed algorithm is available at: https://github.com/chr-secrect/Pest-YOLO.
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Affiliation(s)
- Changji Wen
- College of Information and Technology, Jilin Agricultural University, Changchun, China
- Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China
| | - Hongrui Chen
- College of Information and Technology, Jilin Agricultural University, Changchun, China
| | - Zhenyu Ma
- College of Information and Technology, Jilin Agricultural University, Changchun, China
| | - Tian Zhang
- College of Information and Technology, Jilin Agricultural University, Changchun, China
| | - Ce Yang
- College of Food, Agricultural and Natural Resource Sciences, University of Minnesota, Twin Cities, MN, United States
| | - Hengqiang Su
- College of Information and Technology, Jilin Agricultural University, Changchun, China
- Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China
| | - Hongbing Chen
- College of Information and Technology, Jilin Agricultural University, Changchun, China
- Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China
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46
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Shoaib M, Hussain T, Shah B, Ullah I, Shah SM, Ali F, Park SH. Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease. FRONTIERS IN PLANT SCIENCE 2022; 13:1031748. [PMID: 36275583 PMCID: PMC9585275 DOI: 10.3389/fpls.2022.1031748] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 05/27/2023]
Abstract
Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.
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Affiliation(s)
- Muhammad Shoaib
- Department of Computer Science, CECOS University of Information Technology (IT) and Emerging Sciences, Peshawar, Pakistan
| | - Tariq Hussain
- High Performance Computing and Networking Institute, National Research Council (ICAR-CNR), Naples, Italy
| | - Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Ihsan Ullah
- Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST), Daegu, South Korea
| | - Sayyed Mudassar Shah
- Institute of Computer Science & Information Technology, The University of Agriculture Peshawar, Peshawar, Pakistan
| | - Farman Ali
- Department of Software, Sejong University, Seoul, South Korea
| | - Sang Hyun Park
- Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST), Daegu, South Korea
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47
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Ilyas T, Jin H, Siddique MI, Lee SJ, Kim H, Chua L. DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:983625. [PMID: 36275542 PMCID: PMC9582859 DOI: 10.3389/fpls.2022.983625] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
The emergence of deep neural networks has allowed the development of fully automated and efficient diagnostic systems for plant disease and pest phenotyping. Although previous approaches have proven to be promising, they are limited, especially in real-life scenarios, to properly diagnose and characterize the problem. In this work, we propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant. By taking a single image as input, our algorithm is able to generate detailed descriptive phrases (user-defined) that display the location, severity stage, and visual attributes of all the abnormalities that are present in the image. Our framework is composed of three main components. One of them is a detector that accurately and efficiently recognizes and localizes the abnormalities in plants by extracting region-based anomaly features using a deep neural network-based feature extractor. The second one is an encoder-decoder network that performs pixel-level analysis to generate abnormality-specific severity levels. Lastly is an integration unit which aggregates the information of these units and assigns unique IDs to all the detected anomaly instances, thus generating descriptive sentences describing the location, severity, and class of anomalies infecting plants. We discuss two possible ways of utilizing the abovementioned units in a single framework. We evaluate and analyze the efficacy of both approaches on newly constructed diverse paprika disease and pest recognition datasets, comprising six anomaly categories along with 11 different severity levels. Our algorithm achieves mean average precision of 91.7% for the abnormality detection task and a mean panoptic quality score of 70.78% for severity level prediction. Our algorithm provides a practical and cost-efficient solution to farmers that facilitates proper handling of crops.
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Affiliation(s)
- Talha Ilyas
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju-si, South Korea
- Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju-si, South Korea
| | - Hyungjun Jin
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju-si, South Korea
- Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju-si, South Korea
| | - Muhammad Irfan Siddique
- Department of Plant Science and Plant Genomics and Breeding Institute, Seoul National University, Seoul, South Korea
- Department of Horticultural Science, North Carolina State University, Mountain Horticultural Crops Research and Extension Center, Mills River, United States
| | - Sang Jun Lee
- Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju-si, South Korea
| | - Hyongsuk Kim
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju-si, South Korea
- Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju-si, South Korea
| | - Leon Chua
- Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, United States
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48
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Falaschetti L, Manoni L, Di Leo D, Pau D, Tomaselli V, Turchetti C. A CNN-based image detector for plant leaf diseases classification. HARDWAREX 2022; 12:e00363. [PMID: 36217500 PMCID: PMC9547307 DOI: 10.1016/j.ohx.2022.e00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Identifying diseases from images of plant leaves is one of the most important research areas in precision agriculture. The aim of this paper is to propose an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time image acquisition and classification, equipped with a LCD display showing to the user the classification response in real-time. Experimental results show that this CNN-based image detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 98.10%/95.24% with a very low memory cost (718.961 KB/735.727 KB) and inference time (122.969 ms/125.630 ms) tested on board for the ESCA and the PlantVillage-augmented datasets respectively, allowing the design of a portable embedded system for plant leaf diseases classification. Source files are available at https://doi.org/10.17605/OSF.IO/UCM8D.
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Affiliation(s)
- Laura Falaschetti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Lorenzo Manoni
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Denis Di Leo
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Danilo Pau
- System Research and Applications, STMicroelectronics, Agrate Brianza, Italy
| | - Valeria Tomaselli
- System Research and Applications, STMicroelectronics, Catania, Italy
| | - Claudio Turchetti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
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49
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Sheng X, Wang F, Ruan H, Fan Y, Zheng J, Zhang Y, Lyu C. Disease diagnostic method based on cascade backbone network for apple leaf disease classification. FRONTIERS IN PLANT SCIENCE 2022; 13:994227. [PMID: 36212336 PMCID: PMC9539913 DOI: 10.3389/fpls.2022.994227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising.
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Affiliation(s)
- Xing Sheng
- Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, China
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Fengyun Wang
- Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Huaijun Ruan
- Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yangyang Fan
- Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Jiye Zheng
- Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yangyang Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Chen Lyu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
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Karam C, Awad M, Abou Jawdah Y, Ezzeddine N, Fardoun A. GAN-based semi-automated augmentation online tool for agricultural pest detection: A case study on whiteflies. FRONTIERS IN PLANT SCIENCE 2022; 13:813050. [PMID: 36186035 PMCID: PMC9523729 DOI: 10.3389/fpls.2022.813050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 07/19/2022] [Indexed: 06/16/2023]
Abstract
Deep neural networks can be used to diagnose and detect plant diseases, helping to avoid the plant health-related crop production losses ranging from 20 to 50% annually. However, the data collection and annotation required to achieve high accuracies can be expensive and sometimes very difficult to obtain in specific use-cases. To this end, this work proposes a synthetic data generation pipeline based on generative adversarial networks (GANs), allowing users to artificially generate images to augment their small datasets through its web interface. The image-generation pipeline is tested on a home-collected dataset of whitefly pests, Bemisia tabaci, on different crop types. The data augmentation is shown to improve the performance of lightweight object detection models when the dataset size is increased from 140 to 560 images, seeing a jump in recall at 0.50 IoU from 54.4 to 93.2%, and an increase in the average IoU from 34.6 to 70.9%, without the use of GANs. When GANs are used to increase the number of source object masks and further diversify the dataset, there is an additional 1.4 and 2.6% increase in recall and average IoU, respectively. The authenticity of the generated data is also validated by human reviewers, who reviewed the GANs generated data and scored an average of 56% in distinguishing fake from real insects for low-resolutions sets, and 67% for high-resolution sets.
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Affiliation(s)
- Christophe Karam
- Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Mariette Awad
- Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Yusuf Abou Jawdah
- Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon
| | - Nour Ezzeddine
- Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon
| | - Aya Fardoun
- Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon
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