The Study on Use of Artificial Intelligence in Agriculture - Banthia, Vineet; Chaudaki, Ganesh (Journal of Advanced Research in Applied Artificial Intelligence and Neural Network; Vol 5 No 2 (2021): Journal of Advanced Research in Applied Artificial Intelligence and Neural Network, 2022)
The United Nations FAO (Food and Agriculture Organization) states that the world population would increase by another 2 billion in 2050 while the additional land area under cultivation will only account to 4% at that time. In such circumstance more efficient farming practices can be attained using the recent technological advancements and solutions to current bottlenecks in farming. A direct application of AI (Artificial Intelligence) or machine intelligence across the farming sector could act to be an epitome of shift in how farming is practiced today. Farming solutions which are AI powered enables a farmer to do more with less, enhancing the quality, also ensuring a quick GTM (go-to-market strategy) strategy for crops. The current paper throws a vision of how the diverse sectors of agriculture can be fuelled using AI. It also investigates the AI powered ideas in for future and the challenges anticipated in future. The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost- effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity. How to cite this article: Banthia V, Chaudaki G. The Study on Use of Artificial Intelligence in Agriculture. J Adv Res Appl Arti Intel Neural Netw 2021; 5(2): 18-22.