International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Potato is one of the most important food crops worldwide, and its productivity is greatly affected by various plant diseases. Early and accurate detection of potato plant diseases is essential to reduce crop losses and improve agricultural yield. Traditional disease identification methods rely on manual observation by experts, which is time-consuming, costly, and prone to human error.
This project presents a Potato Plant Disease Classification system using a Convolutional Neural Network (CNN) to automatically identify diseases from leaf images. The proposed model is trained on a dataset containing images of healthy potato leaves and diseased leaves such as Early Blight and Late Blight. Image preprocessing techniques like resizing and normalization are applied to enhance model performance. The CNN model extracts important features from leaf images and classifies them accurately into respective disease categories.
Experimental results show that the CNN-based approach achieves high accuracy and efficiency in disease classification. This system can help farmers and agricultural professionals in early disease detection, enabling timely treatment and reducing crop damage. The proposed solution demonstrates the potential of deep learning techniques in smart agriculture and precision farming.

Downloads: | DOI: 10.17148/IJARCCE.2026.151104