Impact of Automation, Artificial Intelligence and Deep Learning on Agriculture Crop Yield

Every nation is concerned about the growing problem of agriculture automation. It is challenging to supply the food needs of the existing population due to rising numbers, frequent climate change, and scarce resources. Farmers are forced to wreak havoc on the land by applying dangerous pesticides more often since their old techniques cannot keep up with the growing demand. As a result, agricultural practices are significantly impacted, and the land gradually loses its fertility and becomes unproductive. The agriculture sector can benefit from technology like Artificial Intelligence (AI), deep learning, the Internet of Things (IoT), embedded systems, and automation. Artificial neural networks, the Internet of Things, fuzzy logic, machine learning, and other technologies may all be used to automate agricultural systems. Artificial intelligence technology is advancing quickly, and as a result, its employment is in a wide range of fields. Utilizing clever technologies, the agricultural industry has become able to regulate the field environment that is essential to the care of every plant. A suitable atmosphere and appropriate irrigation are provided by the plant's identification and suitable circumstances. In order to increase agriculture yields, it has become important to manage crops in controlled settings like greenhouses that can enhance the output. This chapter focuses on the use of artificial intelligence and IoT technology to improve the productivity of agricultural enterprises. AI technologies might help farmers overcome problems like weeds, pests, and climatic variability that lower output. Numerous uses of AI are now being deployed, such as automatic machine changes for weather forecasting and pest detection. The goal of implementing AI and IoT is to increase the possibility of producing healthy crops by recognizing damaged crops and crop yield growth.

Keywords: Agriculture crop yield, Artificial intelligence, Artificial neural networks, Automation, Drone, Deep learning, Fuzzy logic, Internet of things.