## 5 Challenges of CV in Smart Agriculture
### Edge Deployment Limitations  
The practical implementation of CV in smart agriculture faces significant hardware-level constraints, especially regarding the deployment of deep learning models on edge devices. Unlike industrial or cloud-based environments, smart agricultural systems often operate under limited power, bandwidth, and processing capabilities. Devices such as drones, field robots, and handheld terminals are expected to deliver real-time predictions under stringent energy and memory constraints [198]. This mismatch between model complexity and device capabilities presents a considerable barrier to wide-scale CV adoption in agricultural tasks.  
Deep neural networks (DNNs), especially large-scale architectures such as CNNs and ViTs, demand substantial computational resources for both training and inference. Although these models achieve remarkable performance in laboratory settings, they often require gigabytes of memory and billions of floating-point operations, which exceed the capabilities of edge devices commonly used in field scenarios [300]. For instance, UAVs used for real-time weed detection or crop disease classification must process high-resolution images rapidly while conserving battery life and maintaining flight stability, a trade-off that current models struggle to address effectively.  
Several lightweight network architectures, including backbones like MobileNet, SqueezeNet, and EfficientNet-lite, and specialised object detectors, such as lightweight YOLO variants [309], have been proposed to alleviate these issues by reducing model size and computational load. However, the performance of these lightweight models often drops when they are used in real agricultural environments, which contain diverse and complex visual patterns [298]. Moreover, compression techniques such as pruning, quantisation, and knowledge distillation provide partial solutions, yet they introduce accuracy-efficiency trade-offs that are non-trivial to optimise [303]. These limitations become even more apparent in tasks involving multimodal data fusion, such as combining RGB, multispectral, and thermal images for stress analysis, where model complexity escalates sharply [305].  
Emerging techniques in GenAI provide alternative strategies. For example, generative models can be used to simulate intermediate image features, reducing the need for high-resolution input and consequently lowering computation costs during inference [301]. Yet, such strategies remain in the early research phase and face challenges in reliability and robustness when deployed in the field. Additionally, federated learning frameworks have been considered for decentralised training of agricultural CV models directly on edge devices, thereby enhancing privacy and reducing data transfer burdens. However, real-time synchronisation and bandwidth limitations continue to hinder practical deployments [296].  
Therefore, future research should focus on the co-design of hardware-aware AI models and domain-specific integrated circuits (DSICs) tailored for agricultural CV tasks. This includes developing models optimised for edge inference without sacrificing robustness and accuracy, especially under varying environmental conditions. Model-agnostic optimisation techniques and neural architecture search (NAS) tuned for agricultural datasets could play a crucial role in bridging this computational gap. Overall, addressing edge deployment constraints is vital for achieving scalable and autonomous smart agriculture systems, and it requires a multidisciplinary approach spanning AI model design, embedded systems, and agricultural engineering.