Agriculture Cloud System based Emphatic Data Analysis and Crop Yield Prediction Using Hybrid Artificial Intelligence

Abstract

Abstract In agricultural nations, such as India, where agriculture leads more to India’s Economic growth, it plays a significant part. The prediction of the crop is one of the main tasks in agriculture. Crop prediction methods are employed by detecting different soil parameters and factors connected to the atmosphere for predicting the appropriate crop. The unstable climate exposes farmers to danger in the environment. Therefore the correct history data must be maintained is essential. The data stored may be evaluated to predict agricultural production. In a cloud server, experts analyze sensed data, land type, land, climate, and farmers’ economies with a prediction effect. The method forecasts the use of artificial intelligence algorithms for appropriate crops and fertilizers. A crucial strategy for handling numerous challenges connected to agriculture is the domain of artificial intelligence with its high-quality learning capacity. Technologies to help farmers find better solutions around the world are being created. To benefit from the parallel computational and storage management of huge data sets, the agricultural community must establish an architectural design that would enable the identification of new statistical structures to extract valuable information from data structures. These processes assist to explore the field and different challenges and effectively respond to certain challenges. In the improved integration of diverse data collection types from multiple sources, artificial intelligence offers attractive computing and analytical methods. The main principle of AI and systemic approaches to understanding its use in agriculture are presented in this paper. It also addresses several algorithms for artificial intelligence which may be used to create models to deal with various agricultural problems.