# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 5 Challenges and the Way Forward  
Traditional computer vision methods for extracting manual features are costly and provide limited information, making them difficult to apply in complex agricultural environments. Deep learning can automatically learn information-rich features from data and adapt to complex agricultural environments. As both technologies continue to develop, computer vision and deep learning are playing increasingly important roles in precision agriculture and smart agriculture, much like the eyes and brain. This paper focuses on the agricultural growth management process, reviewing four aspects: crop identification and detection, crop scoring and grading, disease monitoring, and weed detection. Although deep learning and computer vision have brought revolutionary progress to agricultural intelligence, they still face numerous technical bottlenecks and practical obstacles in real-world applications. Figure 9: System operation flowchart. The current urgent challenge is to achieve real-time inference in low-resource environments. The ultimate experimental objective is practical implementation to meet agricultural production needs. In actual farmland management scenarios, the lack of high-performance hardware is a common issue. Most computing devices have limited processing power, making it difficult for large-parameter models to maintain efficient inference speeds. Some farmland areas suffer from poor signal coverage [115], hindering real-time crop disease identification. Additionally, equipment such as sprayers and weeders cannot effectively support high-speed inference models, which remains a critical problem to address. For instance, in ref. [122], the proposed model achieved 85.2% mAP50 during testing. However, when deployed on mobile devices, this metric dropped to 67.4%. To resolve this issue, an increasing number of studies [86; 87] have adopted more lightweight modules to optimize models, improving their deployability in practical applications. Furthermore, field operators are the primary users who need to collect and interpret precise farmland data. Excessively complex operations would increase training costs for farmers. Therefore, reducing model parameters while maintaining accuracy and lowering model complexity is essential. Ensuring reliable inference capabilities in real-world farmland environments with constrained network and computational resources represents a crucial direction for future research. After practical deployment of the method, improving its accuracy becomes the next major challenge. Designing universally generalizable models for multi-scale dynamic targets thus represents a high-priority technical challenge. For crop identification and detection, the varying shapes of crops make multi-scale target detection particularly difficult. The system must simultaneously detect targets ranging from millimeter-scale objects (e.g., pollen) to meter-scale structures (e.g., fruit tree canopies), while accounting for morphological changes throughout the growth cycle [91]. Crops like grapes present additional challenges with severe overlapping and occlusion issues [18]. In disease monitoring, visible damage on crop surfaces varies significantly in size. However, many diseases cause extremely subtle damage that is difficult to detect in early stages. The weak initial symptoms and tiny detection targets constitute major obstacles in disease surveillance. Studies such as [108; 117] focus on extracting more advanced and diverse features. These high-level fused features typically incorporate both global and local crop information, enabling better identification of diseased areas. For pest monitoring, the small size and high mobility of target insects create substantial detection challenges. When processing small insect features, it is crucial to avoid feature loss caused by excessive extraction spans, which may lead to missed detections [109]. Ref.