Solanum Lycopersicum Disease Detection using AlexNet

In recent times, CNNs, or convolutional neural networks have become pivotal in revolutionizing agriculture technology. Early-stage classification, analysis, and detection of leaf diseases, coupled with proactive solutions, play a crucial role in advancing agricultural practices. This paper focuses on the detection of diseases in Solanum Lycopersicum (tomatoes), with a key objective being the early-stage prediction of potential diseases affecting Solanum Lycopersicum leaves. Utilizing a deep learning-based model for image feature extraction, the paper effectively classifies diseased and healthy leaves. The implementation of the CNN-based AlexNet model further enhances performance measures. The primary goal is to achieve an accurate classification of disease-affected leaves through continuous model improvement. Experimental results showcase a remarkable accuracy of 98.83% in detecting Solanum Lycopersicum diseases.