Interactive Agriculture relying on Artificial Intelligence and Convolution Neural Network

Crop production is more complicated than farming, as it involves a lot of labor and very specific humid conditions in both the soil as well as the atmosphere in which to grow them. Farming has been made easier by technology, such as smart irrigation and precise plant nutrition, but these systems have a drawback: they spray water uniformly on all crops, even if some of them need little. An agricultural smart spraying system minimizes the impact on the environment while targeting chemical spray applications. In smart sprayers, which in most cases also have a targeted detection and spraying system, the targeted sensor forms the basis of precision spraying management. Despite the fact that excess water can be sprayed, it is not recommended. A crop can be protected against being affected by possible rots if moisture sensors and float sensors are used as part of the Intelligent Spraying System. Using this technology, it is possible to stop crops from being destroyed by climate change. The crop won't be harmed by pest sickness because of using Convolutional Neural Network (CNN) algorithm. This paper examines the conventional techniques for identifying plant diseases and pests. Agriculture productivity is seriously threatened by agricultural illnesses and insect pests. If pests are not found and identified quickly, they might result in considerable economic losses. So by using CNN those disease are classified in earlier stage and crops are being protected.