Review of The Current State of Deep Learning Applications in Agriculture

The integration of Deep Learning (DL) into agriculture marks a transformative shift towards Agriculture 4.0, addressing critical global challenges such as food security, climate change, and resource scarcity. This comprehensive review synthesizes the current state of DL applications in agriculture, focusing on key domains: precision crop management, livestock monitoring, soil analysis, and water management. DL, primarily leveraging Convolutional Neural Networks (CNNs), excels in tasks like plant disease detection, weed identification, yield prediction, and animal health monitoring by extracting intricate patterns from complex, heterogeneous data sources such as sensors, drones, and satellites. Emerging architectures like Transformers and methodologies such as transfer learning and data fusion further enhance DL’s capability to handle multimodal agricultural data, driving precision and automation. The benefits are substantial—improved accuracy, operational efficiency, resource optimization, and sustainability—yet significant challenges persist. Data scarcity, quality, and bias limit model robustness and generalization, while high computational costs, interpretability issues, and implementation barriers (e.g., cost, infrastructure, expertise) hinder widespread adoption. Looking forward, trends point to deeper integration with IoT and robotics, a data-centric focus, and advancements in Explainable AI (XAI) and edge computing to enable real-time, trustworthy systems. This review underscores DL’s potential to revolutionize farming practices while emphasizing the need for collaborative efforts to overcome data and deployment hurdles. By bridging AI research and practical agriculture, it offers a roadmap for researchers and stakeholders to harness DL for sustainable, efficient food production in an increasingly demanding world.