Groundnut stage wise leaf spot disease dataset

Plant disease detection plays a critical role in ensuring food security and improving crop productivity, particularly in the agricultural sector. Leaf spot disease significantly affects groundnut, a major oilseed crop, resulting in reduced yield and economic loss. In this context, it focuses on the early detection of groundnut leaf spot using Deep Learning (DL) techniques. In this study, a novel Multi-Scale Lightweight Convolutional Block Attention Module (MS-LiteCBAM) MobileNetV2-based model is proposed for accurate classification of the disease at early stages, enabling timely intervention and control measures. The study compares six pre-trained Convolutional Neural Network (CNN) models. The model uses 8050 realtime images to identify four different stages (early leaf spot, moderate leaf spot, severe leaf spot, healthy leaves) groundnut leaf spot. Among these, the proposed MS-LiteCBAM MobileNetV2 model achieved the highest classification training accuracy and validation accuracy at 99.80% and 99.75%, respectively. Furthermore, the model is deployed in a real-time mobile application to facilitate in field level disease detection. The application not only suggests possible treatments and preventive methods based on the classified disease but also stores the predicted results in real-time using Firebase cloud storage. This enables continuous monitoring, supports data-driven decision-making, and provides valuable assistance to farmers for timely action. Overall, the system enhances groundnut disease diagnosis while promoting precision agriculture and sustainable farming practices.