Abstract: An AI-based crop disease detection system makes use of artificial intelligence, specifically computer vision and machine learning, to identify and categorize crop diseases. The main goal is to help farmers and agricultural specialists identify plant diseases early so that they can take quick action and reduce crop losses.These systems usually use photos taken with smartphones or cameras to analyze visible symptoms on plant parts like leaves, stems, or fruits using image processing techniques. Convolutional neural networks (CNNs), a type of deep learning model, are specifically trained to identify visual patterns linked to particular plant diseases or nutrient deficiencies. In certain applications, environmental elements such as humidity and temperature are also included to improve diagnostic precision.
India is an agricultural country. A total of 17% of the GDP comes from agriculture. As a result, it is a significant area of the Indian economy. In terms of global agricultural production, India came in second. Every crop is susceptible to specific diseases that will impact the potential yield's quantity and quality. Crop diseases account for approximately 42% of crop failure and cause the average yield loss for the majority of important food crops. Crop diseases frequently cause the entire crop production to be destroyed. Numerous diseases have an impact on crop production globally. Early disease detection will make it possible to monitor and implement control measures more effectively.

Downloads: | DOI: 10.17148/IJIREEICE.2025.13920