Elevating Crop Quality: AI Neural Networks for Advanced Plant Disease Management and Enhanced Food Security

Abstract

The present research has gone further into diagnosing plant diseases by implementing spectral imaging with the help of Convolutional Neural Networks. Therefore, it is a step beyond traditional RGB imaging and utilizes the captured spectral data of the infrared and ultraviolet spectra for the nascence and sub-clinical level detection of plant diseases. Toward this end, CNN models such as VGG16, Efficient Net, Inception V6, and ResNet-34 were retuned in such a manner that they could process this heterogeneous dataset to enhance the level of diagnostic precision that can be achieved using standard approaches. Hence, through this retuning, it was established how the proposed system improves the performance of these models in all aspects, with particular peaks through the Efficient Net and Inception V6 models, which have maximal test accuracies up to 99.93% for early detection. This has, in a pointed manner increased the potential of the proposed approach to enhancing agricultural practices through the delivery of highly accurate tools for early intervention against diseases, hence ensuring food security worldwide. These results establish new benchmarks in plant pathology diagnostics, representing state-of-the-art imaging technology and deep learning as particularly impactful tools for disease management and mitigation strategies in agriculture.