Abstract: India has risen as a significant player in earlier years, the world’s second-largest producer of paddy, yielding approximately 497.7 million metric tons annualy. Sustaining vast paddy fields demand ongoing attention and upkeep. It is critical to recognize the symptoms and understand how this can effectively control the disease. Therefore, inspired by this research paper, a solution is suggested to train machine learning for identify diseases in paddy plants. The system utilizes rea;- time datasets sourced from the Agriculture Research Institute of Lonavala, which are freely accessible. This system can be used for identification purposes four major paddy plant diseases -Leaf Blast, Leaf Scald, Neck Blast and False smut. This Proposed system, first images are pre-processed and then the custom Convolution Neural Network is used to arranging the images. This project work discusses the methodology for disease identifying diseased leaves at an early At this stage, the suggested automated system can assist farmers in mitigating further harm to their crops.