Research Article
An Empirical Study to Predict Potato Diseases using Machine Learning Algorithms
@INPROCEEDINGS{10.4108/eai.19-12-2025.2362546, author={Akanksha Kaushik and Manvi Breja}, title={An Empirical Study to Predict Potato Diseases using Machine Learning Algorithms }, proceedings={Proceedings of the 1st International Conference on Computational Techniques in Data Science, IICTDS 2025, 19-20 December 2025, Chandigarh, India}, publisher={EAI}, proceedings_a={IICTDS}, year={2026}, month={11}, keywords={Potato Plant Disease Support Vector Machine K-Nearest Neighbor Random Forest Convolutional Neural Network Prediction}, doi={10.4108/eai.19-12-2025.2362546} }- Akanksha Kaushik
Manvi Breja
Year: 2026
An Empirical Study to Predict Potato Diseases using Machine Learning Algorithms
IICTDS
EAI
DOI: 10.4108/eai.19-12-2025.2362546
Abstract
Potato is one of the common and most prevalent crop in multiple countries, but it is prone to numerous diseases that can result in severe productivity losses. The diseases on potatoes need to be early recognized and categorized so that appropriate treatment and control is done. Using a variety of machine learning techniques, we looked at how effectively five generic potato diseases such as early-blight, late-blight, common scab, black-scurf, and soft-rot are classified. Our research indicates that potato infections can be effectively classified by applying machine learning algorithms, which can help with disease monitoring and control in real time. In this research, we offer a system that classifies two distinct diseases in potato plants according to the health of their leaves using machine learning to produce an accurate classification system. The proposed system utilizes supervised and unsupervised classification models. This experiment exhibited a 95% accuracy rate after implementing Convolutional Neural Network which highlights the effectiveness and accuracy in detecting potato diseases early.