Implementation of Artificial Intelligence and Deep Learning for Optimizing Sustainable Agricultural Production - Syaharuddin Syaharuddin (Masoem University, 2026)
This study aims to explore the application of Artificial Intelligence (AI), deep learning technologies, and machine learning approaches for optimizing sustainable agricultural production, particularly in agricultural price forecasting. Using a Systematic Literature Review approach, this research collects and analyzes literature indexed in Scispace, Google Scholar, DOAJ, and Scopus published between 2014 and 2024. The findings indicate that AI, deep learning, Support Vector Machine (SVM), and Relevance Vector Machine (RVM) technologies hold significant potential in improving operational efficiency, food security, and forecasting accuracy in the agricultural sector. These technologies can support various aspects of agriculture, including agricultural price forecasting, crop yield prediction, plant disease detection, and more efficient natural resource management. However, the implementation of these technologies still faces several significant challenges, such as inconsistent data quality, high implementation costs, ethical issues related to data usage, and infrastructure limitations in rural areas. This study concludes that while these technologies offer considerable potential, their successful implementation depends heavily on addressing these challenges and developing policies that support the sustainable adoption of technology in the agricultural sector.