MACHINE LEARNING AND DEEP LEARNING FOR SUSTAINABLE AGRICULTURE

Abstract

Recent digitalization has included increasing elements of artificial intelligence and Machine Learning into agriculture and Deep Learning to address the challenges brought about by population growth, Cli- mate change (CC) and Resource Limitation (RL). The present study comprehensively deals with the areas of potential applications of AI techniques. The innovations range from upstream to downstream in agricultural production, with an emphasis on those that conform to Climate-smart (CS) agricultural practices. A review of research articles was carried out, with the Application of Machine Learning and Deep Learning in crop selection, monitoring and land management, water, soil and nutrient malabsorp- tion, management, weed control, harvest and post-harvest practices, managing pests and insects, and soil management. The results highlight that ML and DL enable the analysis of complicated datasets, thereby informing data-driven decision-making, reducing dependence on subjective expertise, and enhanc- ing farm management strategies. Machine Learning and Deep Learning also offer immense opportunities in increasing agriculture productivity, sustainability, and resilience. By highlighting data-driven insights and embracing innovative technologies, the agricultural sector can transition toward more efficient, en- vironmentally sustainable, and economically feasible approaches to farming to contribute towards food globally.