Analysis of Agricultural Toolset based on Artificial Intelligence

Abstract

One of the industries that are most crucial to humanity is agriculture. Agriculture mechanization is the major issue facing all countries today. As the world's population is expanding at an incredibly fast rate, there is an increasing demand for food. To fulfill the expanding demand, farmers will need to apply chemical pesticides more often than they already do. The soil is harmed by this. The land continues to be unproductive and barren as a result of this having a substantial influence on agricultural activities. Several mechanization strategies, including deep learning, machine learning, and artificial intelligence, are covered in this article. It is crucial to use new technologies at various stages of the agro-based supply chain due to several long-term challenges for the agricultural industry and various factors, such as population growth, global warming, technological advancement, and the condition of environmental assets (water, etc.). Examples include automated farm equipment processes, the use of sensing devices and satellite data for distant locations, artificial intelligence, and machine learning for forecasting weather patterns. Crop diseases, inadequate storage management, chemical usage, weed control, insufficient irrigation, and poor water management are just a few problems the agricultural sector is facing. Using the range of strategies covered, each of these problems might be handled. It has been demonstrated that automating farming procedures increases soil productivity and improves soil fertility. To get a quick overview of how automation is currently being used in agriculture, this paper examines the work of numerous researchers. In the current study, we highlight the key uses of AI and Ml techniques in farming and highlight the undeniably rising trend in the implementation of these techniques to advance the agriculture sector.