Review of The Current State of Deep Learning Applications in Agriculture

Abstract

The integration of Deep Learning (DL) into agriculture, a cornerstone of Agriculture 4.0, addresses global challenges like food security, climate change, and resource scarcity. This review explores DL’s applications in precision crop management, livestock monitoring, soil analysis, and water management. Leveraging Convolutional Neural Networks (CNNs), DL excels in tasks such as plant disease detection, weed identification, yield prediction, and animal health monitoring by analyzing complex data from sensors, drones, and satellites. Advanced architectures like Transformers, along with techniques like transfer learning and data fusion, enhance DL’s ability to process multimodal agricultural data, boosting precision and automation. DL offers significant benefits, including improved accuracy, operational efficiency, resource optimization, and sustainability. However, challenges persist, including data scarcity, quality issues, and biases that reduce model robustness. High computational costs, limited interpretability, and implementation barriers—such as expensive infrastructure and lack of expertise—restrict widespread adoption, particularly in resource-constrained regions. Future trends include deeper integration with IoT and robotics, a focus on data-centric approaches, and advancements in Explainable AI (XAI) and edge computing for real-time, trustworthy systems. This review highlights DL’s transformative potential in agriculture while stressing the need for collaborative efforts to address data and deployment challenges. By aligning AI research with practical farming needs, DL can drive sustainable, efficient food production to meet growing global demands, offering a roadmap for researchers and stakeholders to advance smart agriculture.