Enhancing Leaf Disease Detection in Short - Statured Crops Using a Smart Agribot With Sensor Fusion and Image Analysis

The early detection and management of plant diseases in short-statured crops such as beans, black gram, cowpea, groundnut, and tomato are crucial for minimizing yield losses and improving agricultural resilience in regions like the Karnataka-Tamil Nadu border. This study presents the design, development, and evaluation of a convolutional neural network (CNN)-based plant disease detection model integrated into a low-height rover system tailored for small-statured crop fields. A dataset of 18,256 annotated images spanning 23 classes of healthy and diseased leaf samples was compiled, pre-processed, and used to train a multi-layered CNN architecture. While batch-level prediction of 18,256 test samples revealed overfitting evidenced by a uniform misclassification of all samples into a single class—the system demonstrated reliable disease identification during real-time field deployment. The rover’s performance was further enhanced through the integration of soil moisture sensors, enabling preliminary analysis of environmental correlations with disease occurrence. The model and its hardware platform were evaluated qualitatively and quantitatively, with findings presented using confusion matrices, class distribution charts, and visual documentation from field trials. Limitations such as reduced effectiveness under fluctuating weather conditions, limited battery life in large-scale fields, and incompatibility with taller crops were acknowledged. Recommendations include integrating solar energy systems and improving data augmentation techniques to enhance robustness. This study contributes significantly to the underexplored domain of precision agriculture for short-statured crops, offering an accessible, scalable framework for early disease diagnosis using AI and embedded systems. Despite its constraints, the system presents a viable tool for smallholder farmers, fostering sustainable agricultural practices. Future research should address model generalization, environmental adaptability, and integration with cloud-based farm management systems. This work underscores the potential of AI-driven solutions in transforming low-input farming ecosystems by offering context-specific technological interventions rooted in local agronomic realities.