Identification of Plant Disease Stage Through Leaf Analysis using Potato Plant

Plant ailments can threaten farmers' livelihoods and their ability to produce enough food. Early detection and diagnosis are crucial for effective management and control of plant diseases. As leaf is one of the essential part of plants, its analysis can lead to detection of any problem on the plant, its analysis is a promising approach for disease stage prediction, as it can be used to spot minute changes in leaf physiology and appearance that might happen even before the start of obvious symptoms. In this research a machine learning system is introduced that can accurately determine the stage of potato leaf diseases automatically. The model involves steps such, as gathering images preparing them for analysis identifying features combining them and making predictions. By blending features from established models using the suggested Convolutional Neural Network (CNN) the models’ capabilities are enhanced. The utilization of vision transformers aids in making predictions. The model has been tested using a dataset containing images of early blight, late blight and healthy leaves. Through the integration of segmentation and feature fusion techniques this method achieves an accuracy rate of 95.28%. These results validate that the combined framework significantly improves the detection and prediction accuracy of potato plant diseases showing promise, for real world applications.