## 5 Challenges of CV in Smart Agriculture
### Model Generalisation and Regional Adaptation  
Achieving robust model generalisation is a key challenge in applying CV techniques to smart agriculture due to the high variability of agricultural environments. Crops grow under drastically different soil conditions, climates, and cultivation practices across regions,leading to significant visual heterogeneity. This variation often hinders the transferability of models trained on specific datasets, a challenge echoed across multiple studies in both robotics and deep learning for agriculture [301; 304; 306]. For instance, models trained in temperate, well-lit greenhouses may fail when deployed in open fields with variable light, occlusion, and crop morphology [295].  
To address this, researchers have explored domain adaptation and transfer learning strategies, which allow pretrained deep networks to be fine-tuned with smaller region-specific datasets. However, these methods are constrained when data scarcity coincides with domain shift, as often happens in less-developed agricultural zones [293; 297]. GenAI has been proposed as a means to bridge these gaps by producing synthetic images to simulate rare or underrepresented conditions [294]. Yet, the synthetic samples often suffer from a lack of realism or fail to represent critical edge cases, thus limiting their utility in enhancing generalisation [299]. Moreover, the diverse growth stages of and seasonal variations in crops introduce temporal inconsistencies that standard CNN-based models are not well-equipped to handle without extensive retraining [296].  
Another important aspect is generalisation across different agricultural tasks, such as weed detection, fruit harvesting, and disease classification. Each task may require a distinct set of features and decision rules. For example, a weed detection model optimised for cotton fields [306] is unlikely to be effective in wheat or maize fields due to differences in plant spacing, structure, and colouration. Similarly, models used for phenotyping in aquaculture show limited transferability to terrestrial livestock monitoring due to differences in texture and movement patterns [293]. This fragmentation necessitates task-specific retraining or the adoption of modular AI systems that can adapt their feature representation dynamically [300].  
Large foundation models, including transformers and vision-language models, have shown promise in improving generalisation by leveraging broad contextual understanding [307]. However, their application in agriculture is still in its early stages, and their benefits have not yet been fully realised in real-world settings due to limited labelled data and computing resources [303]. Furthermore, some studies suggest that XAI could support model adaptation by revealing which features are relied upon during inference, potentially identifying overfitting to spurious cues in the training data [298; 305].  
The lack of benchmarking standards further exacerbates the problem. Many research works use proprietary datasets or measure success using inconsistent metrics, which makes it difficult to assess the true generalisation capabilities of proposed methods. This gap highlights the need for open, standardised multi-location datasets that represent the diversity of real-world agricultural environments [308].  
In conclusion, while the generalisation ability of CV models in agriculture has improved through advanced training strategies and the emergence of large-scale architectures, true robustness across tasks and regions remains elusive. Future efforts must integrate synthetic data, domain adaptation, modular architectures, and explainability tools while prioritising the creation of benchmark datasets that reflect the diversity of global agricultural systems.