A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification

Anil Bhujel, Na-Eun Kim, Elanchezhian Arulmozhi, Jayanta Kumar Basak and Hyeon-Tae Kim
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Anil Bhujel: Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea
Na-Eun Kim: Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea
Elanchezhian Arulmozhi: Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea
Jayanta Kumar Basak: Smart Farm Research Center, Gyeongsang National University, Jinju 52828, Korea
Hyeon-Tae Kim: Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, Korea

Agriculture, 2022, vol. 12, issue 2, 1-18

Abstract: Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).

Keywords: attention module; convolutional neural networks; lightweight network; tomato disease; disease detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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