Early Detection of Pomegranate Disease Using Machine Learning and Internet of Things

Pomegranate is extremely established fruit in Asian country and earns high profit. But due to dynamical atmospheric condition like variations in temperature, precipitation additionally as relative humidity pomegranate plants are infected by numerous diseases which ends in reduction of the assembly. Crop diseases will be uncovered in its early stages with the assistance of projected system by using Hidden Markov Model and sensor network, which is able to offer alerts to the farmer and therefore the professional. The Pomegranate fruit and leaves are laid low with numerous diseases aggravated by plant life, microorganism and therefore the atmospheric condition. These diseases include bacterial blight, fruit spot, fruit rot and leaf spot. Air temperature, air humidness, leaf wetness amount and soil wetness are the aspects that have severe impact on the disease development in pomegranate. The system uses numerous parameters together with temperature, humidity and uses gsm module for wireless digital communication.