AI-Driven Crop Disease Diagnosis for Sustainable Agriculture Using Convolutional Neural Networks

New opportunities opened by artificial intelligence (AI) help to improve agriculture and develop new methods of solving traditional problems. In this study, a system of the crop disease diagnosis based on AI and CNN is proposed and designed. It is important to have timely, or better still early, diagnosis of the diseases that affect crops in order to protect food security as well as reduce losses. Most of the conventional procedures of diagnosing diseases, the processes are slow, exhaustive and involve high risks of human interference. In effort to achieve high accuracy in the identification of disease, the proposed system utilizes a deep learning model known as CNN that can perform image recognition and classification. Through many exercises, the model is trained to identify different crop images that have been infected by different diseases and the model differentiates between healthy and infected crops with efficiency. The capacity of the system to process big data sets in real-time assists farmers in avoiding over-heavy use of pesticides when making decisions on disease control in large fields out of the need to seek help from experts who may prescribe un-sustainable control measures. The findings of this research show how AI has benefits to increase productivity, reduce risks and support the development of a more reliable farming communities. The conclusions drawn can therefore be used to support the adoption of AI technologies in the farming sector surrounding the world thwarting challenges like climate change, resource depletion, and population concern.