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.
