Lightweight attention-enhanced EfficientDet for tomato leaf detection and disease classification in real-world conditions

Abstract

Tomatoes are a critical crop for global food security, offering substantial nutritional and economic value. Their widespread availability makes them a key component in combating malnutrition. However, diseases affecting tomato plants significantly threaten agricultural productivity. While numerous deep learning-based approaches have been proposed to address this challenge, many rely on datasets collected in controlled environments. As a result, these models often underperform in real-field conditions due to complex backgrounds and variable lighting. Accurate leaf detection is a crucial first step in enabling automated disease monitoring under such conditions, as it facilitates precise downstream analysis. In this study, we present Lite-AttnEffDet, a lightweight and efficient leaf detection model built upon EfficientDet and enhanced with lightweight attention mechanisms. Specifically, we incorporate an efficient channel attention module into the detection head to strengthen channel-wise feature emphasis, thereby improving leaf localization in cluttered, real-world scenes. The proposed Lite-AttnEffDet was evaluated on the FieldPlant dataset, which contains tomato plant images captured in real agricultural settings. Experimental results demonstrate that Lite-AttnEffDet achieves a mAP@IoU = 0.50 of 0.930±0.014 and a mean Recall@IoU = 0.50 of 0.860±0.021 over five independent runs, outperforming the baseline EfficientDet-D0 and other conventional detectors while requiring only 2.6 GFLOPs and increasing the number of trainable parameters by just 2.56% over the baseline. The improved leaf detection capability directly benefits downstream classification, where convolutional neural network-based models achieve up to 88% accuracy, a notable improvement over the 82% accuracy observed when using raw images. These results underscore the robustness of Lite-AttnEffDet in real-world field environments, making it a promising solution for plant health monitoring and precision agriculture.