Cotton Disease Detection Using Deep LearningSubmitted in Fulfillment Of The Requirements For TheDegree Master of Science in Artificial Intelligence

Abstract

The main objective of the research is to diagnose cotton Disease through Image Analysis Using Deep learning. Where it is traditional diagnosis method relies on manual inspection, which is labor-intensive and prone to human mistakes. To overcome this problem, has been proposed Image Analysis Using deep learning technologies, particularly (CNNs), cotton pictures can be diagnosed with high precision to enable faster and more accurate disease detection. The main parts of the affected sheets are also highlighted by the type of disease in the database. This allows for early detection of the disease, reducing agricultural losses, increasing productivity, and improving the sustainability of cotton farming. Approximately 80-90% of cotton diseases affect leaves, causing up to 16% budget loss and potentially 30-50% crop loss without control. This research explores AI techniques from cotton leaves images.