CNN- Based Disease Detection For Cauliflower

Abstract

Convolutional Neural Networks (CNN) have received a lot of attention for disease identification in agricultural crops like cauliflower because of its capacity to evaluate visual patterns in images automatically and reliably. This paper investigates a CNN-based framework for identifying and classifying diseases that affect cauliflower leaves, using deep learning techniques to process and evaluate images of infected and healthy plants. By training the CNN on a dataset of annotated photos, the model is able to extract significant properties such as texture, color, and shape abnormalities caused by diseases such as fungal infections, bacterial spots, or vitamin shortages. The suggested system detects illnesses with high accuracy, providing a fast, reliable, and scalable option for farmers and agronomists. This technology has the potential to improve crop management, reduce losses, and promote sustainable agriculture practices by allowing for early diagnosis and action. The suggested method is intended to help farmers and agricultural professionals diagnose diseases more quickly, reliably, and affordably. Early detection leads to timely response, lowering crop losses and pesticide consumption. This promotes sustainable agricultural practices. This study demonstrates the potential of CNNs to change disease management in cauliflower agriculture and lays the groundwork for extending similar strategies to other crop.