Deep Learning and Sustainability in Agriculture: A Systematic Review

Abstract

Agriculture plays a pivotal role in the global economy, and with the increasing human population, there is substantial pressure to enhance agricultural productivity while minimizing environmental impacts. Digital agriculture and precision farming have emerged as innovative fields leveraging data-intensive approaches to optimize agricultural operations. However, there is a gap in understanding how machine learning (ML) techniques can be effectively integrated across various agricultural domains, including crop management, livestock monitoring, soil analysis, and water management. To address this gap, this study provides a comprehensive review of the application of ML techniques in different agricultural domains. By analyzing publications from 2016 to 2023, we identify key trends, challenges, and future directions for ML in agriculture. The methodology involves categorizing 40 papers from top reputable journals and conferences into four main domains: crop, livestock, soil, and water, and examining the specific ML techniques applied in each domain. Our results show that crop management is the most researched domain, with significant use of machine learning, deep learning, ensemble learning, and transfer learning techniques. Livestock monitoring and soil analysis also employ advanced ML methods, though they face unique challenges related to data quality and model generalizability. Water management benefits from ML for optimizing resource use but encounters difficulties in data integration and scalability. The significance of this study lies in its holistic analysis of ML applications in agriculture, highlighting the most effective techniques and identifying critical challenges that need to be addressed. By providing insights into current research trends and potential future directions, this review serves as a valuable resource for researchers and practitioners aiming to enhance agricultural productivity through advanced ML technologies.