Development of a Plant Disease Detection System Using Deep Learning and Organic Solutions for Sustainable Agriculture

Plant diseases have become a significant issue for farmers around the world, impacting crop growth and food production. Traditionally, manual inspection and specialist knowledge have been used to detect these diseases, which can be time-consuming and inaccurate. Additionally, the use of chemical pesticides to treat these diseases can harm the environment and human health. In order to address these issues, this study develops an automated system that swiftly and precisely detects plant illnesses using cutting-edge technology, particularly deep learning models. The system analyzes images of crops such as sugarcane, tomatoes, wheat, rice, and corn, to detect various diseases. Once a disease is identified, the system also suggests natural, organic solutions made from ingredients like neem oil, garlic, turmeric, and cow urine. These natural remedies offer a more secure and sustainable substitute for artificial pesticides. A set of photos showing various plant diseases was used to test the system. According to the data, the approach outperformed conventional methods in precisely and rapidly identifying plant illnesses. Apart from identifying diseases, the system assists farmers by offering environmentally friendly treatment alternatives, which lessens their dependency on dangerous chemicals. This research demonstrates how combining technology and organic farming practices can help modernize how plant diseases are managed. It shows how farmers can benefit from faster, more reliable disease detection and treatment suggestions that are better for both crops and the environment. Future improvements will include expanding the system's database and making it available to more farmers through mobile apps.