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Agriculture is a backbone of the national economy. This project focuses on the advancement of a sophisticated plant leaf disease recognition method that harnesses the capabilities of deep transfer learning algorithms for precise and efficient disease classification. This project adopts a pathogen-centric approach, utilizing deep transfer learning models. The foremost intend of this project is to discover the plant illnesses by pathogens and the system is integrated into a smart irrigation arrangement through the use of Internet of Things technology with deep transfer learning algorithm. The deep transfer learning improves disease diagnosis. It enables the system to learn from a wide variety of data, improving its ability to recognize different plant diseases. The Internet of Things component makes sure that the smart irrigation arrangement has smooth connectivity and communication. Not only can this intelligent approach diagnose ailments, but it also makes a substantial contribution to effective irrigation management. It helps to promote sustainable agriculture by facilitating the quick and targeted responses made available by IOT. This project promotes early intervention, which improves crop health and yield.
Parameshwari et al. (Fri,) studied this question.