Deep learning-based phenotyping of lettuce diseases using Efficient-FBM-FRMNet for precision agriculture

Abstract

Lettuce (Lactuca sativa), a widely cultivated leafy vegetable, is highly susceptible to bacterial and fungal infections that severely reduce yield and quality. Rapid and accurate disease identification is therefore essential for precision agriculture and sustainable crop management. This study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection. The model integrates EfficientNetB4 with dilated convolutions, a Feature Bottleneck Module (FBM) for redundancy reduction, a Reasoning Engine for higher-order semantic inference, and a Feature Refinement Module (FRM) for enhanced generalization. The framework was trained and validated on a publicly available dataset of 2,813 lettuce leaf images (bacterial, fungal, and healthy classes) using stratified 5-fold cross-validation. The proposed Efficient-FBM-FRMNet achieved an overall accuracy of 97.5%, outperforming baseline CNNs such as EfficientNetB4, ResNet50, and DenseNet121. It demonstrated superior precision (96.0%), recall (96.6%), and F1-score (97.0%), confirming its robustness and consistency across multiple folds. Statistical significance analysis (p < 0.05) verified that the performance gains were not due to random variation. The integration of FBM, Reasoning Engine, and FRM enhances discriminative feature learning, interpretability, and stability while reducing computational cost (8.2 MB model size, 23 ms inference). These results demonstrate the model’s potential for real-world deployment in greenhouse monitoring, UAV-based surveillance, and mobile diagnostic systems, contributing to sustainable, AI-driven precision agriculture.