1
|
Ong P, Jian J, Li X, Zou C, Yin J, Ma G. Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 324:125001. [PMID: 39180971 DOI: 10.1016/j.saa.2024.125001] [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: 12/12/2023] [Revised: 07/14/2024] [Accepted: 08/18/2024] [Indexed: 08/27/2024]
Abstract
Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.
Collapse
Affiliation(s)
- Pauline Ong
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
| | - Jinbao Jian
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China.
| | - Xiuhua Li
- School of Electrical Engineering, Guangxi University, Nanning 530005, China; Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530005, China.
| | - Chengwu Zou
- Guangxi Key Laboratory of Sugarcane Biology and College of Agriculture, Guangxi University, Nanning 530005, China; State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China.
| | - Jianghua Yin
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China
| | - Guodong Ma
- College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China
| |
Collapse
|
2
|
Chen S, Lu X, Fang H, Perumal AB, Li R, Feng L, Wang M, Liu Y. Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging. ABIOTECH 2024; 5:281-297. [PMID: 39279856 PMCID: PMC11399517 DOI: 10.1007/s42994-024-00169-1] [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: 12/26/2023] [Accepted: 05/04/2024] [Indexed: 09/18/2024]
Abstract
Bakanae disease, caused by Fusarium fujikuroi, poses a significant threat to rice production and has been observed in most rice-growing regions. The disease symptoms caused by different pathogens may vary, including elongated and weak stems, slender and yellow leaves, and dwarfism, as example. Bakanae disease is likely to cause necrosis of diseased seedlings, and it may cause a large area of infection in the field through the transmission of conidia. Therefore, early disease surveillance plays a crucial role in securing rice production. Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied. In this study, a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease. Phenotypic data were obtained on the 9th, 15th, and 21st day after rice infection to explore the physiological and biochemical performance, which helps to deepen the research on the disease mechanism. Hyperspectral data were obtained over these same periods of infection, and a deep learning model, named Rice Bakanae Disease-Visual Geometry Group (RBD-VGG), was established by leveraging hyperspectral imaging technology and deep learning algorithms. Based on this model, an average accuracy of 92.2% was achieved on the 21st day of infection. It also achieved an accuracy of 79.4% as early as the 9th day. Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance. Collectively, the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease, thereby providing an efficient avenue for disease prevention and control. Supplementary Information The online version contains supplementary material available at 10.1007/s42994-024-00169-1.
Collapse
Affiliation(s)
- Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Xuqi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Hongda Fang
- State Key Laboratory of Rice Biology and Breeding, Zhejiang University, Hangzhou, 310058 China
| | - Anand Babu Perumal
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Computational Modeling and Nanoscale Processing Unit, National Institute of Food Technology, Entrepreneurship and Management - Thanjavur, Ministry of Food Processing Industries, Thanjavur, 613005 India
| | - Ruyue Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Mengcen Wang
- State Key Laboratory of Rice Biology and Breeding, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| |
Collapse
|
3
|
Lu Y, Nie L, Guo X, Pan T, Chen R, Liu X, Li X, Li T, Liu F. Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116704. [PMID: 38996646 DOI: 10.1016/j.ecoenv.2024.116704] [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: 03/17/2024] [Revised: 06/14/2024] [Accepted: 07/06/2024] [Indexed: 07/14/2024]
Abstract
Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.
Collapse
Affiliation(s)
- Yi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Linjie Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xinyu Guo
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xunyue Liu
- College of Advanced Agricultural Sciences, Zhejiang A & F University, Hangzhou 311300, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Tingqiang Li
- Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| |
Collapse
|
4
|
Xiao T, Yang L, Zhang D, Cui T, Zhang X, Deng Y, Li H, Wang H. Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134723. [PMID: 38815392 DOI: 10.1016/j.jhazmat.2024.134723] [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: 03/25/2024] [Revised: 05/06/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize production, and maintaining the field environment. In this study, we utilized maize crops treated with the widely used nicosulfuron herbicide and their hyperspectral images to develop the HerbiNet model. After 4 d of nicosulfuron treatment, the model achieved an accuracy of 91.37 % in predicting toxicity levels, with correlation coefficient R² values of 0.82 and 0.73 for soil plant analysis development (SPAD) and water content, respectively. Additionally, the model exhibited higher generalizability across datasets from different years and seasons, which significantly surpassed support vector machines, AlexNet, and partial least squares regression models. A lightweight model, HerbiNet-Lite, exhibited significantly low complexity using 18 spectral wavelengths. After 4 d of nicosulfuron treatment, the HerbiNet-Lite model achieved an accuracy of 87.93 % for toxicity prediction and R² values of 0.80 and 0.71 for SPAD and water content, respectively, while significantly reducing overfitting. Overall, this study provides an innovative approach for the early and accurate detection of nicosulfuron toxicity within maize fields.
Collapse
Affiliation(s)
- Tianpu Xiao
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Li Yang
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
| | - Dongxing Zhang
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Tao Cui
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Xiaoshuang Zhang
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Ying Deng
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Hongsheng Li
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| | - Haoyu Wang
- College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China
| |
Collapse
|
5
|
Ji X, Zhou Z, Gouda M, Zhang W, He Y, Ye G, Li X. A novel labor-free method for isolating crop leaf pixels from RGB imagery: Generating labels via a topological strategy. COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024; 218:108631. [DOI: 10.1016/j.compag.2024.108631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
|
6
|
Zhou C, Miao P, Dong Q, Li D, Pan C. Multiomics Explore the Detoxification Mechanism of Nanoselenium and Melatonin on Bensulfuron Methyl in Wheat Plants. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:3958-3972. [PMID: 38363203 DOI: 10.1021/acs.jafc.3c08429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Combining nanoselenium (nano-Se) and melatonin (MT) was more effective than treatment alone against abiotic stress. However, their combined application mitigated the toxic effects of bensulfuron methyl, and enhanced wheat growth and metabolism has not been studied. Metabolomics and proteomics revealed that combining nano-Se and MT markedly activated phenylpropanoid biosynthesis pathways, elevating the flavonoid (quercetin by 33.5 and 39.8%) and phenolic acid (vanillic acid by 38.8 and 48.7%) levels in leaves and roots of wheat plants. Interstingly, beneficial rhizosphere bacteria in their combination increased (Oxalobacteraceae, Nocardioidaceae, and Xanthomonadaceae), which positively correlated with the enhancement of soil urease and fluorescein diacetate enzyme activity (27.0 and 26.9%) and the allelopathic substance levels. To summarize, nano-Se and MT mitigate the adverse effects of bensulfuron methyl by facilitating interactions between the phenylpropane metabolism of the plant and the beneficial microbial community. The findings provide a theoretical basis for using nano-Se and MT to remediate herbicide-contaminated soil.
Collapse
Affiliation(s)
- Chunran Zhou
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, People's Republic of China
| | - Peijuan Miao
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, People's Republic of China
| | - Qinyong Dong
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, People's Republic of China
| | - Dong Li
- School of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan 570228, People's Republic of China
| | - Canping Pan
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, People's Republic of China
| |
Collapse
|
7
|
Dey B, Ferdous J, Ahmed R. Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables. Heliyon 2024; 10:e25112. [PMID: 38322954 PMCID: PMC10844259 DOI: 10.1016/j.heliyon.2024.e25112] [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: 06/29/2023] [Revised: 12/07/2023] [Accepted: 01/20/2024] [Indexed: 02/08/2024] Open
Abstract
Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity. The models namely Support vector machine, XGBoost, Random forest, KNN, and Decision Tree were trained using yields of individual data sets of 11 agricultural and 10 horticultural crops, as well as combined yield of both agri-horticultural crops. The results strongly suggest to evaluate individual data sets separately for each crop category rather than using combined the data sets of both categories for better predictions. Comparing the five ML models, the XGBoost demonstrated the highest level of accuracy. The precision rates of XGBoost for recommending agricultural crops, horticultural crops, and a combination of both were 99.09 % (AUC 1.0), 99.3 % (AUC 1.0), and 98.51 % (AUC 0.99), respectively. This non-intrusive method for generating crop recommendations in diverse environmental conditions holds the potential to provide valuable insights for the development of a user-friendly AI cloud-based interface. Such an interface would enable rapid decision-making for optimal fertilizer applications and the selection of suitable crops for cultivation at specific sites.
Collapse
Affiliation(s)
- Biplob Dey
- Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
- Center for Research in Environment, iGen and Livelihood (CREGL), Sylhet 3114, Bangladesh
| | - Jannatul Ferdous
- Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Romel Ahmed
- Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
- Center for Research in Environment, iGen and Livelihood (CREGL), Sylhet 3114, Bangladesh
| |
Collapse
|
8
|
Rolfe M, Hayes S, Smith M, Owen M, Spruth M, McCarthy C, Forkan A, Banerjee A, Hocking RK. An AI based smart-phone system for asbestos identification. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132853. [PMID: 37918071 DOI: 10.1016/j.jhazmat.2023.132853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
Asbestos identification is a complex environmental and economic challenge. Typical commercial identification of asbestos involves sending samples to a laboratory where someone learned in the field uses light microscopy and specialized mounting to identify the morphologically distinct signatures of Asbestos. In this work we investigate the use of a portable (30x) microscope which works with a smart phone camera to develop an image recognition system. 7328 images from over 1000 distinct samples of cement sheet from Melbourne, Australia were used to train a phone-based image recognition system for Asbestos identification. Three common CNN's were tested ResNet101, InceptionV3 and VGG_16 with ResNet101 achieving the best result. The distinctiveness of Asbestos was found to be identified correctly 90% of the time using a phone-based system and no specialized mounting. The image recognition system was trained with ResNet101 a convolutional neural network deep learning model which weights layers with a residual function. Resulting in an accuracy of 98.46% and loss of 3.8% ResNet101 was found to produce a more accurate model for this use-case than other deep learning neural networks.
Collapse
Affiliation(s)
- Michael Rolfe
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Samantha Hayes
- Agon Environmental Pty, Ltd 63-85 Turner Street, Port Melbourne, VIC 3207, Australia
| | - Meaghan Smith
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Matthew Owen
- Identifibre Pty Ltd., 67 Atherton Road, Oakleigh, VIC 3166, Australia
| | - Michael Spruth
- Agon Environmental Pty, Ltd 63-85 Turner Street, Port Melbourne, VIC 3207, Australia
| | - Chris McCarthy
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Abdur Forkan
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Abhik Banerjee
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Rosalie K Hocking
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia.
| |
Collapse
|
9
|
Bu Y, Hu J, Chen C, Bai S, Chen Z, Hu T, Zhang G, Liu N, Cai C, Li Y, Xuan Q, Wang Y, Su Z, Xiang Y, Gong Y. ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness. Sci Rep 2024; 14:2568. [PMID: 38297076 PMCID: PMC11224382 DOI: 10.1038/s41598-024-51668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.
Collapse
Affiliation(s)
- Yuanpeng Bu
- Institute of Vegetables, Key Laboratory of Vegetable Legumes Germplasm Enhancement and Southern China of the Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jinxuan Hu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Cheng Chen
- Zhejiang Yuncheng Information technology Co Ltd., Hangzhou, China
| | - Songhang Bai
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Zuohui Chen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Tianyu Hu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Guwen Zhang
- Institute of Vegetables, Key Laboratory of Vegetable Legumes Germplasm Enhancement and Southern China of the Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Na Liu
- Institute of Vegetables, Key Laboratory of Vegetable Legumes Germplasm Enhancement and Southern China of the Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Chang Cai
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Yuhao Li
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Qi Xuan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Ye Wang
- Faculty of Engineering, Lishui University, Lishui, China
| | - Zhongjing Su
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
| | - Yun Xiang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China.
| | - Yaming Gong
- Institute of Vegetables, Key Laboratory of Vegetable Legumes Germplasm Enhancement and Southern China of the Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
| |
Collapse
|
10
|
Li R, Chen S, Matsumoto H, Gouda M, Gafforov Y, Wang M, Liu Y. Predicting rice diseases using advanced technologies at different scales: present status and future perspectives. ABIOTECH 2023; 4:359-371. [PMID: 38106429 PMCID: PMC10721578 DOI: 10.1007/s42994-023-00126-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
Collapse
Affiliation(s)
- Ruyue Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Department of Nutrition and Food Science, National Research Centre, Giza, 12622 Egypt
| | - Yusufjon Gafforov
- Central Asian Center for Development Studies, New Uzbekistan University, Tashkent, 100000 Uzbekistan
| | - Mengcen Wang
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| |
Collapse
|
11
|
Niu Z, Rehman T, Young J, Johnson WG, Yokoo T, Young B, Jin J. Hyperspectral Analysis for Discriminating Herbicide Site of Action: A Novel Approach for Accelerating Herbicide Research. SENSORS (BASEL, SWITZERLAND) 2023; 23:9300. [PMID: 38067672 PMCID: PMC10708448 DOI: 10.3390/s23239300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/08/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023]
Abstract
In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)-the general mechanisms affecting plant processes-has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.
Collapse
Affiliation(s)
- Zhongzhong Niu
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; (Z.N.); (T.Y.)
| | - Tanzeel Rehman
- Department of Biosystems Engineering, Auburn University, Auburn, AL 36849, USA;
| | - Julie Young
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA; (J.Y.); (W.G.J.); (B.Y.)
| | - William G. Johnson
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA; (J.Y.); (W.G.J.); (B.Y.)
| | - Takayuki Yokoo
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; (Z.N.); (T.Y.)
- Health and Crop Sciences Research Laboratory, Sumitomo Chemical Co., Ltd., Takarazuka 665-8555, Hyogo, Japan
| | - Bryan Young
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA; (J.Y.); (W.G.J.); (B.Y.)
| | - Jian Jin
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; (Z.N.); (T.Y.)
| |
Collapse
|
12
|
Gouda M, Ghazzawy HS, Alqahtani N, Li X. The Recent Development of Acoustic Sensors as Effective Chemical Detecting Tools for Biological Cells and Their Bioactivities. Molecules 2023; 28:4855. [PMID: 37375410 PMCID: PMC10304203 DOI: 10.3390/molecules28124855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
One of the most significant developed technologies is the use of acoustic waves to determine the chemical structures of biological tissues and their bioactivities. In addition, the use of new acoustic techniques for in vivo visualizing and imaging of animal and plant cellular chemical compositions could significantly help pave the way toward advanced analytical technologies. For instance, acoustic wave sensors (AWSs) based on quartz crystal microbalance (QCM) were used to identify the aromas of fermenting tea such as linalool, geraniol, and trans-2-hexenal. Therefore, this review focuses on the use of advanced acoustic technologies for tracking the composition changes in plant and animal tissues. In addition, a few key configurations of the AWS sensors and their different wave pattern applications in biomedical and microfluidic media progress are discussed.
Collapse
Affiliation(s)
- Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Department of Nutrition & Food Science, National Research Centre, Dokki, Giza 12622, Egypt
| | - Hesham S. Ghazzawy
- Date Palm Research Center of Excellence, King Faisal University, Al Ahsa 31982, Saudi Arabia
- Central Laboratory for Date Palm Research and Development, Agriculture Research Center, Giza 12511, Egypt
| | - Nashi Alqahtani
- Date Palm Research Center of Excellence, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| |
Collapse
|
13
|
Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds. Food Chem 2023; 404:134503. [DOI: 10.1016/j.foodchem.2022.134503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/28/2022] [Accepted: 10/01/2022] [Indexed: 11/06/2022]
|
14
|
Sanaeifar A, Yang C, de la Guardia M, Zhang W, Li X, He Y. Proximal hyperspectral sensing of abiotic stresses in plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160652. [PMID: 36470376 DOI: 10.1016/j.scitotenv.2022.160652] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.
Collapse
Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Ce Yang
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, United States.
| | - Miguel de la Guardia
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain.
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| |
Collapse
|
15
|
Lou Z, Quan L, Sun D, Li H, Xia F. Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 844:157071. [PMID: 35798120 DOI: 10.1016/j.scitotenv.2022.157071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1-5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1-5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520-525 nm peak, 570-655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.
Collapse
Affiliation(s)
- Zhaoxia Lou
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| | - Longzhe Quan
- College of Engineering, Northeast Agricultural University, Harbin 150030, China; College of Engineering, Anhui Agricultural University, Anhui 230036, China.
| | - Deng Sun
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| | - Hailong Li
- College of Engineering, Anhui Agricultural University, Anhui 230036, China
| | - Fulin Xia
- College of Engineering, Northeast Agricultural University, Harbin 150030, China
| |
Collapse
|
16
|
Zhang C, Zhou L, Xiao Q, Bai X, Wu B, Wu N, Zhao Y, Wang J, Feng L. End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses. PLANT PHENOMICS 2022; 2022:9851096. [PMID: 36059603 PMCID: PMC9394116 DOI: 10.34133/2022/9851096] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/03/2022] [Indexed: 11/07/2022]
Abstract
Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.
Collapse
Affiliation(s)
- Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yiying Zhao
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Junmin Wang
- Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| |
Collapse
|
17
|
Cao Y, Yuan P, Xu H, Martínez-Ortega JF, Feng J, Zhai Z. Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution. FRONTIERS IN PLANT SCIENCE 2022; 13:963170. [PMID: 35909723 PMCID: PMC9328758 DOI: 10.3389/fpls.2022.963170] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.
Collapse
Affiliation(s)
- Yifei Cao
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Peisen Yuan
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - José Fernán Martínez-Ortega
- Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Jiarui Feng
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Zhaoyu Zhai
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| |
Collapse
|
18
|
Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer. REMOTE SENSING 2022. [DOI: 10.3390/rs14091997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.
Collapse
|
19
|
Gouda M, He Y, Bekhit AED, Li X. Emerging Technologies for Detecting the Chemical Composition of Plant and Animal Tissues and Their Bioactivities: An Editorial. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092620. [PMID: 35565969 PMCID: PMC9105901 DOI: 10.3390/molecules27092620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 11/16/2022]
Abstract
Integrating physical and chemical technologies for the characterization and modification of plants and animal tissues has been used for several decades to improve their detection potency and quality [...].
Collapse
Affiliation(s)
- Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Department of Nutrition & Food Science, National Research Centre, Dokki, Giza 12422, Egypt
- Correspondence: or (M.G.); (Y.H.); (X.L.)
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Correspondence: or (M.G.); (Y.H.); (X.L.)
| | - Alaa El-Din Bekhit
- Department of Food Sciences, University of Otago, Dunedin 9054, New Zealand;
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Correspondence: or (M.G.); (Y.H.); (X.L.)
| |
Collapse
|
20
|
Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10020045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.
Collapse
|