Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging
In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1434163/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846136531329220608 |
|---|---|
| author | Xin Liu Xin Liu Kaixin Meng Kaixing Zhang Wujie Yang Jiutao Yang Lingyang Feng Haoran Gong Chang’an Zhou |
| author_facet | Xin Liu Xin Liu Kaixin Meng Kaixing Zhang Wujie Yang Jiutao Yang Lingyang Feng Haoran Gong Chang’an Zhou |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production. |
| format | Article |
| id | doaj-art-43ba722f48d84b56a0a1dd79f21bcf0f |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-43ba722f48d84b56a0a1dd79f21bcf0f2024-12-09T04:25:57ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14341631434163Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imagingXin Liu0Xin Liu1Kaixin Meng2Kaixing Zhang3Wujie Yang4Jiutao Yang5Lingyang Feng6Haoran Gong7Chang’an Zhou8Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, ChinaCollege of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, ChinaCollege of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, ChinaCollege of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, ChinaCrop Technology Promotion Department 1, Shandong Agricultural Technology Extension Center, Jinan, Shandong, ChinaCrop Technology Promotion Department 1, Shandong Agricultural Technology Extension Center, Jinan, Shandong, ChinaPeking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, ChinaCollege of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, ChinaCollege of Agronomy, College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, Shandong, ChinaIn order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production.https://www.frontiersin.org/articles/10.3389/fpls.2024.1434163/fullhyperspectral feature extractioncrop disease detectionmachine learningintelligent optimizationnon-invasive identification |
| spellingShingle | Xin Liu Xin Liu Kaixin Meng Kaixing Zhang Wujie Yang Jiutao Yang Lingyang Feng Haoran Gong Chang’an Zhou Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging Frontiers in Plant Science hyperspectral feature extraction crop disease detection machine learning intelligent optimization non-invasive identification |
| title | Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging |
| title_full | Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging |
| title_fullStr | Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging |
| title_full_unstemmed | Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging |
| title_short | Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging |
| title_sort | discrimination of leaf diseases in maize soybean intercropping system based on hyperspectral imaging |
| topic | hyperspectral feature extraction crop disease detection machine learning intelligent optimization non-invasive identification |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1434163/full |
| work_keys_str_mv | AT xinliu discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT xinliu discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT kaixinmeng discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT kaixingzhang discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT wujieyang discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT jiutaoyang discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT lingyangfeng discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT haorangong discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging AT changanzhou discriminationofleafdiseasesinmaizesoybeanintercroppingsystembasedonhyperspectralimaging |