Enhancing Lightweight Mango Disease Detection Model Performance through a Combined Attention Module

Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the convolutional block attention module (CBAM), the coordinate attention (CA) module, and the squeeze-and-excitation (SE) module. The dataset is constructed by eight classes, including seven classes of disease conditions and one class of health conditions. The experimental result shows that the proposed lightweight model has the optimal results, which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny. While the mAP50:95 also achieved the highest results compared to other models, including YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5, and YOLOv7-Tiny. The advantage of the proposed lightweight model is the adaptability that supports it in constrained environments, such as edge computing systems. This proposed model can support a robust, precise, and convenient precision agriculture system for the user.