## 5 Challenges of CV in Smart Agriculture
### Environmental Variability and Robustness  
Smart agriculture is intrinsically tied to the outdoor, dynamic, and often unpredictable nature of farm environments. As such, environmental variability--ranging from changing illumination and weather fluctuations to background clutter--poses a persistent challenge to the robustness of CV systems. While deep learning models have achieved significant success in controlled settings, their performance frequently degrades in real-world agricultural scenes due to a lack of adaptability to shifting conditions [299].  
Lighting changes, including variations in sunlight, cloud coverage, and shadow patterns, significantly affect image quality and model performance. For example, tasks such as fruit ripeness detection or leaf disease classification rely heavily on colour and texture features that can be distorted under inconsistent lighting conditions [302]. Similarly, seasonal changes influence plant morphology and field appearance, rendering models trained in one season ineffective in another. Furthermore, rain, dust, and wind can introduce occlusions and motion blur, compromising image clarity and annotation accuracy [293].  
Real-time systems such as autonomous tractors or harvest robots demand not only accurate but also rapid predictions. Yet, the underlying models must adapt to environmental feedback loops, which is difficult to achieve using static models trained on offline datasets. Current solutions often involve data augmentation strategies, such as simulating environmental effects during training or applying adaptive image enhancement. However, these approaches offer limited generalisation, especially when confronted with rare or extreme conditions [295]. Temporal and spatial domain shifts further complicate the problem, where models trained in one region fail when deployed in another with different soil types, vegetation structures, or pest prevalence [294].  
Some progress has been made by integrating multi-sensor fusion (e.g., combining visual, thermal, and LiDAR data) to improve robustness, though at the cost of increased system complexity and energy demands [297]. Transfer learning and continual learning approaches have also been explored to adapt models over time. However, these methods require frequent retraining, which is computationally expensive and sometimes impractical for edge-based deployment [307]. Few-shot learning and meta-learning frameworks offer a promising direction for rapid adaptation under low-resource scenarios but are still underdeveloped in agricultural applications [308].  
To build truly adaptive and robust agricultural CV systems, research must advance in the direction of self-supervised learning, domain generalisation, and online learning. Future models must be capable of continual self-improvement and uncertainty estimation to alert users when predictions become unreliable. Additionally, to accurately assess how a model will perform in the real world, evaluation benchmarks should shift. Instead of relying on accuracy metrics from static datasets, they should prioritise testing models in dynamic, real-world field conditions [295]. Overall, enhancing CV robustness under environmental variability is key to sustainable and reliable smart agriculture.