Background/Purpose: The agriculture sector is the backbone of every nation which contributes to the global economy. The implementation of technology in agriculture has brought revolutionary development in its outcome. Due to this, a drastic improvement in the global economy from the agricultural sector is expected. Moreover, the implementation of artificial intelligence (AI) improves the productivity of farmers giving solutions to various challenges faced by the farmers. The various AI tools that are developed for the agriculture sector include precision farming, predictive analytics, automated machinery, smart irrigation systems, crop and soil monitoring, supply chain optimization, weather forecasting, and livestock management. Adopting AI in agriculture faces several challenges despite its long-term benefits. The high upfront costs to be invested in implementing AI technology make it difficult for small-scale and developing farmers to invest in AI. Implementing the above technology needs technical skills, fast internet connectivity, and costlier equipment. Due to the lack of the above-mentioned requirements, the AI technologies that are meant for agriculture do not reach the farmers. This results in the wastage of resources for AI without the outcome. Considering the above issues an appropriate simplified model is proposed that facilitates the adaptation of the AI technology by small and medium-scale farmers in their agriculture to improve the performance.
Objective: The objective of this paper is to review the various journals related to the implementation of AI in Agriculture and to study the various issues related to its implementation. It also aims at identifying the research gap which will help to develop a model suitable for the end like small-scale and medium-scale farmers.
Design/Methodology/Approach: A systematic literature review was conducted by gathering and examining relevant literature from international and national journals, conferences, databases, and other resources accessed via Google Scholar and various search engines.
Findings/Result: The agriculture sector, crucial to every nation's economy, has seen revolutionary advancements through technology, especially AI. AI tools like precision farming, predictive analytics, and smart irrigation promise to enhance productivity and address various agricultural challenges. However, high implementation costs, resistance to new technologies, and lack of necessary infrastructure hinder widespread adoption among small-scale and developing farmers. To overcome these obstacles, a model is proposed to effectively support farmers in adopting AI technologies to boost agricultural performance.
Originality/Value: The implementation of AI and ML tools in agriculture from diverse sources is done. This area needs study due to recent challenges faced by small and medium-scale farmers in the implementation of AI and ML tools in agriculture. The information acquired will help to create a new model by improving the outcomes of the existing scenario.
Paper Type: Literature Review.
