Development of a Multi-Sensor Fusion Framework for Early Detection and Monitoring of Corn Plant Diseases

The accurate and timely identification of plant diseases is critical for ensuring global food supplies and agricultural sustainability. Traditional approaches for identifying and dealing with corn plant diseases are time-consuming, labor-intensive, and frequently result in late diagnoses. In regard, the proposed work provides a unique multi-sensor fusion system to revolutionize the detection of corn plant disease. The proposed system combines data from visible and near-infrared cameras, thermal imaging, hyperspectral imaging, and environmental sensors implementing an integrated approach. The multi-modal data fusion enables advanced machine learning models to identify and classify diseases with greater accuracy. The demonstrated results indicate the system’s excellent performance, with an accuracy of 95%, sensitivity of 96%, and specificity of 94%, exceeding previous methods significantly. Furthermore, the system exhibits consistent accuracy across several growth phases, ensuring effective disease management. Real-time monitoring metrics demonstrate a 5-minute alert reaction time and a 20% reduction in disease management costs. The proposed methodology is a game changer, overcoming the drawbacks of existing methodologies and offering a solid framework for improving crop health, yield, and resource allocation.