Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems.

Agricultural AI agents play a crucial role in the evolution of smart agriculture, from single-point automated applications to intelligent systems driven by tasks, collaborative decision-making, and closed-loop execution. However, their practical implementation still faces key challenges, such as heterogeneous agricultural data processing, insufficient cross-scenario generalization ability, complexity of multi-agent collaboration, difficulties in integrating software and hardware, and insufficient security and trust guarantees in real agricultural environments. This paper presents a systematic review of the architecture design, business processes, key technologies, and future challenges of agricultural AI agents. Agricultural AI agents are classified into two types: virtual agricultural AI agents and embodied agricultural AI agents. The paper summarizes a four-layer system architecture consisting of the infrastructure layer, agent management layer, agent collaboration layer, and application layer. The paper also analyzes the model capabilities required by agricultural AI agents from four typical business dimensions: perception and state understanding, knowledge memory and experience management, reasoning decision-making and task planning, and collaborative execution and resource scheduling. This research shows that technologies such as multimodal perception, knowledge graphs, retrieval-enhanced generation, digital twins, reinforcement learning, and multi-agent collaboration can provide important support for agricultural AI agents to enhance their environmental understanding, knowledge reuse, autonomous decision-making, and physical execution capabilities. Future research should focus on robust perception in open environments, long-term memory and knowledge evolution, reliable multi-agent collaboration, edge-cloud collaborative deployment, and secure and trustworthy human–machine collaboration. Integrating agricultural domain knowledge with intelligent agent technology is an important direction for promoting the large-scale, adaptive, and sustainable application of agricultural AI agents.