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| Main Authors: | , , , , , , , , |
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| Format: | Artículo Open Access |
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Wiley
2024
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| Online Access: | https://scijournals.onlinelibrary.wiley.com/doi/10.1002/ps.8317 |
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| _version_ | 1867006480021979136 |
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| author | Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu |
| author_facet | Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu |
| collection | Wiley Open Access |
| contents | Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu Pest Management Science AbstractBACKGROUNDCrop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop‐specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets.RESULTSOur results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61–98.76%), Mixed (90.76–92.31%), and Field (68.56–73.81%). Although CSMs were found to reduce inter‐crop disease misclassifications, they also led to a slight increase in intra‐crop misclassifications.CONCLUSIONOur findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field‐specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry. 10.1002/ps.8317 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/ps.8317 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ps_8317 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu Pest Management Science Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches Qi Tian Gang Zhao Changqing Yan Linjia Yao Junjie Qu Ling Yin Hao Feng Ning Yao Qiang Yu Pest Management Science AbstractBACKGROUNDCrop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop‐specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets.RESULTSOur results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61–98.76%), Mixed (90.76–92.31%), and Field (68.56–73.81%). Although CSMs were found to reduce inter‐crop disease misclassifications, they also led to a slight increase in intra‐crop misclassifications.CONCLUSIONOur findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field‐specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry. 10.1002/ps.8317 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches |
| topic | Pest Management Science |
| url | https://scijournals.onlinelibrary.wiley.com/doi/10.1002/ps.8317 |