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Background <p>Field-scale assessment of chili leaf curl complex presents a significant diagnostic challenge, as both chili leaf curl virus (ChiLCV) and mite infestations produce visually overlapping symptoms difficult to distinguish by untrained personnel. This diagnostic confusion frequently leads to inappropriate application of either insecticides or acaricides, resulting in economic losses and environmental concerns. To address this issue, we propose SCA-MobiPlant, an improved MobileNetV3-Small model integrated with a novel multistage Squeeze-and-Excitation Coordinate Attention (SCA) fusion mechanism, designed for accurate differentiation of these apparently similar symptoms and precise field assessment of the disease.</p> Results <p>The proposed model effectively focuses on subtle diagnostic features including leaf texture, petiole elongation, and irregular curling patterns to achieve reliable classification. The multistage SCA fusion module demonstrated superior performance, achieving 99.64% accuracy, 99.61% precision, 99.64% recall, and 99.62% F1-score through K = 5 cross-validation, outperforming other attention modules such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA). Gradient-Weighted Class Activation Mapping (Grad-CAM) provided visual interpretability of the model’s decision-making process. Comparative evaluation against state-of-the-art architectures, including EfficientNetB0, ResNet50, VGG19 and YOLO advanced series, confirmed the computational efficiency of the proposed model for mobile deployment.</p> Conclusions <p>The final system, termed SCA-MobiPlant, has been successfully implemented on smartphones, along with a Disease Incidence (DI) calculation module, enabling rapid and accurate field assessment of the disease. This facilitates appropriate intervention strategies while minimizing unnecessary pesticide use. The study highlights the potential of lightweight, attention-enhanced models for real-world plant disease diagnostics, particularly in resource-constrained agricultural settings.</p> Graphical abstract <p></p>

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SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Samrat Paul,
  • Venu Emmadi,
  • Mehulee Sarkar,
  • Shubhajyoti Das,
  • Anirban Roy,
  • Parimal Sinha

摘要

Background

Field-scale assessment of chili leaf curl complex presents a significant diagnostic challenge, as both chili leaf curl virus (ChiLCV) and mite infestations produce visually overlapping symptoms difficult to distinguish by untrained personnel. This diagnostic confusion frequently leads to inappropriate application of either insecticides or acaricides, resulting in economic losses and environmental concerns. To address this issue, we propose SCA-MobiPlant, an improved MobileNetV3-Small model integrated with a novel multistage Squeeze-and-Excitation Coordinate Attention (SCA) fusion mechanism, designed for accurate differentiation of these apparently similar symptoms and precise field assessment of the disease.

Results

The proposed model effectively focuses on subtle diagnostic features including leaf texture, petiole elongation, and irregular curling patterns to achieve reliable classification. The multistage SCA fusion module demonstrated superior performance, achieving 99.64% accuracy, 99.61% precision, 99.64% recall, and 99.62% F1-score through K = 5 cross-validation, outperforming other attention modules such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA). Gradient-Weighted Class Activation Mapping (Grad-CAM) provided visual interpretability of the model’s decision-making process. Comparative evaluation against state-of-the-art architectures, including EfficientNetB0, ResNet50, VGG19 and YOLO advanced series, confirmed the computational efficiency of the proposed model for mobile deployment.

Conclusions

The final system, termed SCA-MobiPlant, has been successfully implemented on smartphones, along with a Disease Incidence (DI) calculation module, enabling rapid and accurate field assessment of the disease. This facilitates appropriate intervention strategies while minimizing unnecessary pesticide use. The study highlights the potential of lightweight, attention-enhanced models for real-world plant disease diagnostics, particularly in resource-constrained agricultural settings.

Graphical abstract