Computational Approaches for Identifying Potato Plant Pathogens

Potato cultivation is a vital component of global agriculture, providing a significant source of nutrition and income. However, the threat of diseases affecting potato crops remains a persistent challenge for farmers. In our study, we present a novel approach to automatically classify diseases in potato plants using advanced deep learning methods. The proposed approach makes use of Convolutional Neural Networks (CNNs), that perform efficiently for image processing applications. The dataset utilized in our research comprises high-resolution Images depicting diverse disease symptoms in potato plants, including late blight, early blight, and the oomycete pathogen Phytophthora infestans, a destructive ailment that impacts potato crops. To automatically discern between healthy and diseased plants, we implement a multi-layered CNN architecture that learns discriminative characteristics of images. This research significantly contributes to precision agriculture by providing an automated and efficient solution for identifying potato diseases. The proposed method in disease classification not only facilitates prompt disease management but also enables precise treatments, thereby reducing reliance on chemical interventions. The practical implications of these findings extend to farmers, agronomists, and researchers involved in sustainable and resilient potato cultivation.