Automating yellow rust disease identification in wheat using artificial intelligence

Plant disease has long been one of the major threats to world food security due reduction in crop yield and quality. Accurate precise diagnosis plant diseases a significant challenge. Cost-effective automated computational systems for would facilitate advancements agriculture. The objective this paper is explore computer vision based Artificial Intelligence method automating identification yellow rust improve accuracy identification. dataset 2000 images wheat leaf were collected real life experimental conditions ICAR-Indian Agricultural Research Institute, New Delhi season during January-April, 2019. Based on our experiment, we propose deep learning-based approach detect healthy leaves infected crop. experiments are implemented python with PyCharm IDE, utilizing Keras learning library backend TensorFlow. proposed model achieves 97.3% testing 98.42% as training accuracy. developed can be improved further by it larger size future. In future, AI models using datasets. Also, these used providing automatic advisory services farmers, thereby, adding much needed assistance overloaded extension experts.