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

Plant phenotyping relevance

レタス葉画像から病害状態を推定する深層学習フレームワークを開発し、公開データセット上で交差検証・ベースライン比較により性能を評価しており、植物表現型取得手法が中心である。

Code and data availability

The paper's phenotyping measurements are based entirely on a public Kaggle lettuce plant disease image dataset (2,813 images, bacterial/fungal/healthy), cited with an explicit public URL matching an allowed URL. No author code or model checkpoints are stated as available.