Plant Disease Detection Using Intelligence of Things

Abstract

To increase productivity and crop growth, farmers must implement automated disease monitoring rather than relying on spot-checks. Physicians favor manual monitoring offer unsatisfactory results because the naked eye approach takes longer to identify, which means disease also has to be expertly identified. We in this paper incorporated new, state-of the art diagnostic and clinical testing methods to examine disease on both the leaves and fruits of citrus trees. The development of an automated diagnostic system for distinguishing between a diseased and healthy plant was described in this paper. Plant disease can greatly diminishes the yield and quality of crop production. It has been developed to provide a way to detect the presence of disease in the plants using this study. A self-monitoring device has sensors including temperature, humidity, color, and growth patterns as its basis. Due to the many aspects of the soil, the parameters of temperature, humidity, and colour are commonly used to classify disease in plants.