A Review of Intentions to Use Artificial Intelligence in Big Data Analytics for Thailand Agriculture

This study explains the essentials of artificial intelligence (AI) in big data analytics for the agriculture industry during the COVID-19 pandemic. The systematic review approach was employed for the documentary and analysed using content analysis. In addition, the literature and previous studies were obtained from various research articles on EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect. The criteria for inclusion were studies that were related to AI in big data analytics for agriculture, were published in English, and were peer-reviewed. Five independent reviewers assessed search results, extracted data, and evaluated the studies' quality to summarise and report the findings. The results reveal that the COVID 19 pandemic has increased the utility of AI in big data for agricultural business operations. For instance, AI-enabled supply chain management enables businesses to accurately predict demand spikes and decreases and adjust material volumes and routes. AI can collect more extensive data, which can assist the production team in predicting more accurate delivery times and inventory adjustments. Adaptability, high performance, precision, and cost-effectiveness are the central concepts of AI in agriculture. AI agriculture applications include soil, crop, weed, and disease management. The direct application of AI throughout the agricultural sector could represent a paradigm shift in agricultural practices. AI-powered farming solutions enable farmers to accomplish more with fewer resources, enhance crop quality, and guarantee a rapid GTM (go-to-market) crop strategy. Thus, AI adoption is based on the TAM model's perceived ease of use, usefulness, and social influence. The recommendation is to research qualitative and quantitative approaches for further study and clarification of the essence of AI in the agriculture industry