DEEP LEARNING APPROACHES IN PRECISION AGRICULTURE: A COMPREHENSIVE REVIEW OF CROP CLASSIFICATION, DISEASE DETECTION, AND WEED DETECTION TECHNIQUES

摘要

This paper presents a systematic and comprehensive review of deep learning (DL) methodologies used in precision agriculture (PA). It focuses on three critical application areas in particular: plant classification, plant disease detection, and weed detection. The study covers 93 peer-reviewed papers published between 2020 and 2025 and indexed in the SCI and SCI-Expanded indexed WoS database. Of these, 68 studies addressed disease detection, 13 focused on plant classification, and 12 examined weed detection strategies. The review describes a wide range of DL architectures, including Convolutional Neural Networks (CNNs), Residual Networks (ResNet), You Only Look Once (YOLO), Image Transformers (ViT), and various hybrid frameworks. A large number of models demonstrated exceptional performance with classification accuracies reaching up to 99.64% and precision and sensitivity values exceeding 98%. Studies have evaluated a wide range of datasets such as PlantVillage, COCO, and privately acquired RGB/UAV imagery, and a variety of sensor platforms such as drones, smartphones, hyperspectral, and LiDAR systems. Moreover, transfer learning and ensemble learning approaches have been frequently adopted to enhance generalization capabilities and model robustness. The integration of DL models with advanced technologies such as unmanned aerial vehicles (UAVs), unmanned ground robots (UGRs), depth-sensing cameras, and mobile-based platforms facilitates automation in agricultural monitoring, disease diagnosis, and yield prediction. This review not only consolidates the current technological developments, but also analyzes the emerging trends, methodological gaps, and possible directions for the advancement of sustainable, data-driven agricultural systems using artificial intelligence.