Comparison and analysis of Rice Blast disease identification in Greenhouse Controlled Environment and Field Environment using ML Algorithms

In many Countries, agriculture is the prime source of their income and the growth of the GDP. In older days, most of the agriculture fields are cultivated with the help of cow dung as fertilizer and yield the productivity. But right now due to the growing population in this globe we are in the necessity to increase the food demands. As mentioned earlier, in older days without not having problems in the field and population also limited, so the food is sufficient, right now the population is increased and the agricultural farms are facing lot of problems like farm land are sold, disease and pests etc., Lot of pesticides also applied in the farms in the current scenario. To overcome these issues the proposed method is based on the greenhouse controlled environment to cultivate the rice crops and compared with the farm field crops. The smart farming method or controlled environment method proven thatwithout applying pesticides the crop growth rate is good. In the Proposed methods, images are collected from the farms and detected with the help of k-means clustering algorithm. The disease got detected 100% accurate in both the cases. The image analysis algorithm got applied in the agriculture field in various applications and proved 100% efficiency.