Mitigating Economic losses in Potato farming: Accurate identification of Potato leaves diseases

Potatoes, a globally recognized vegetable, are integral to agriculture worldwide. However, potato leaf diseases pose significant threats to potato yields. In this paper, we propose a methodology utilizing image processing techniques to identify and classify these diseases promptly, minimizing financial losses for farmers. Leveraging machine learning, particularly Convolutional Neural Networks (CNN), our model accurately detects potato leaf diseases from images. We meticulously engineer each step, from data preprocessing to model evaluation, utilizing TensorFlow and Keras. Through dataset visualization, partitioning, and augmentation, we prepare our model for robust training. The CNN architecture, chosen for its superior image classification capabilities, demonstrates remarkable accuracy in distinguishing between healthy and diseased potato leaves. Our methodology showcases the potential of technology to revolutionize agriculture by enabling early disease detection and efficient crop management.