# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 6 Conclusions  
Deep learning and computer vision technologies have brought revolutionary changes to agricultural production. This paper reviews and summarizes relevant articles from the past decade, focusing on four aspects: crop identification, quality grading, disease monitoring, and weed detection. Section 2 categorizes common methods used in computer vision to obtain information, while Section 3 introduces commonly used deep learning technologies in the agricultural field. Through a systematic review of relevant research, we find that these technologies have moved from the laboratory to the field: models based on YOLO and Transformers achieve over 90% accuracy in crop recognition in complex environments. The combination of hyperspectral imaging and 3D-CNN keeps grading errors for agricultural products within 3%. Multimodal data fusion significantly enhances disease diagnosis capabilities, while lightweight detection models provide real-time solutions for precise weed control.  
However, the application of deep learning and computer vision in agriculture also faces numerous challenges. Scarce and expensive labeled data constrain model generalization capabilities, and the early detection of millimeter-sized pests and diseases remains a technical challenge. Algorithms need to be deployed on actual devices to ensure the practical implementation of the technology, but hardware investments result in prohibitively high costs. Additionally, complex real-world environments limit the effectiveness of algorithms and hardware. Despite these challenges, the field still holds significant potential for future development. First, specialized large-scale models for agriculture should be developed, leveraging pre-training with massive datasets to enhance transfer learning capabilities and improve model generalization. Second, novel sensing technologies, deep learning techniques, and agriculture should be more comprehensively integrated, such as quantum sensing and nano-imaging technologies, as well as various more efficient attention mechanisms. Finally, experiments and production should progress together. By designing lightweight models to lower application barriers, research findings can be more widely adopted and applied. It is foreseeable that when technological maturity aligns with industrial demand, agriculture will enter an era of smart agriculture, where the entire process from sowing to harvest will be driven by autonomous decision-making. This leap in production efficiency is a crucial safeguard for food security and sustainable development--and a top priority for human societal progress.  
Conceptualization, Z.C.; investigation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, Z.C. and S.S.; supervision, X.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Project 333 of Jiangsu Province, grant number 501100017534.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.