## 5 Challenges of CV in Smart Agriculture
### Data scarcity and Quality  
The effectiveness of CV systems in smart agriculture is closely tied to the availability of high-quality image data for training, validation, and deployment. However, the scarcity of large-scale, diverse, and well-annotated agricultural image datasets is a persistent challenge. Unlike general CV tasks with abundant datasets like ImageNet or COCO, agricultural data is often domain-specific, seasonal, and geographically constrained, which limits the quantity and diversity of available data [293; 294]. Collecting agricultural image data, especially in field conditions, involves logistical difficulties, variable lighting, weather disturbances, and inconsistent object appearances, making standardised data acquisition tough [198; 295].  
The annotation process presents another bottleneck. Labelling agricultural images requires expert knowledge to accurately classify plant diseases, pests, or growth stages, making it time-consuming and cost-intensive, especially for tasks requiring pixel-level segmentation or temporal tracking across image sequences [296; 297]. Intra-class variability caused by natural growth diversity, disease symptoms, and environmental conditions further complicates the labelling task, often requiring repeated efforts and multi-expert validation to ensure reliability and consistency [298].  
To tackle the limited annotated datasets, recent advances have turned to GenAI, particularly GANs and diffusion models, which can synthesise synthetic agricultural images for data augmentation [299]. These techniques can theoretically enhance model robustness and generalisation by producing varied samples that mimic rare conditions or underrepresented classes. However, practical adoption faces issues such as questionable realism and biological validity of generated samples, especially for fine-grained tasks like disease spotting or pest identification, where subtle textural patterns are critical [300; 301]. Synthetic data may also lack the environmental and phenotypic diversity needed to capture the full range of field variability, leading to potential overfitting on artificial patterns not present in real-world images [302; 303].  
Another concern is the domain shift between laboratory-generated datasets and real-world deployment scenarios. CV models trained on controlled or synthetic datasets may fail to generalise in the field due to differences in sensor quality, background clutter, and unanticipated anomalies [304; 305]. This highlights the demand for domain adaptation and transfer learning strategies that can leverage limited real data while benefiting from large synthetic corpora. Semi-supervised and self-supervised learning paradigms, though promising, remain underexplored in agriculture and need further validation under diverse field conditions [306; 307].  
In response to these challenges, hybrid approaches combining real annotated images, weakly labelled data, and generative augmentation are gaining attention. These approaches aim to reduce dependency on expensive annotations while enhancing the diversity and volume of training data. Meanwhile, data standardisation protocols, open-access platforms, and community-driven datasets like PlantVillage or Global Wheat Head Dataset represent key efforts in democratising agricultural CV development [308].  
Despite these developments, data challenges remain a fundamental limitation in deploying CV in smart agriculture. Future efforts should prioritise data collection and annotation strategies, as well as the development of rigorous evaluation benchmarks, transparency in dataset reporting, and ethical considerations related to data ownership, privacy, and representativeness [307].