# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Actual Case Applications  
After a model algorithm is proposed, it should be applied to actual agricultural practices to promote agricultural economic development. The flowchart for deploying computer vision to practical applications is shown in Figure 9. In this process, factors such as initial costs, environmental constraints, and implementation effects need to be comprehensively considered. Taking a smart farm (33 hectares) in Jiangsu as an example, this farm adopts a collaborative scheme of \"drone inspection + ground precision spraying\" to realize intelligent weed management. The farm collects vegetation spectral features through a multispectral camera and uses a detection model to distinguish crops from 12 common weed species, guiding the variable spray device for targeted application. In the specific deployment process, a Matrice 350 RTK drone and a multispectral camera are used for large-scale mapping of weed distribution. The ground edge computing equipment adopts an industrial computer equipped with an NVIDIA RTX A5000 graphics card to ensure real-time model inference and decision-making. A variable spray device controlled by intelligent valves is used for targeted application. In addition, there are licenses for the detection model and data management platform to realize algorithm support and data storage. The above constitutes the initial cost of the farm, which is approximately USD 40,000. After equipment deployment, the implementation of intelligent systems still requires farmers to conduct actual operations. Farmers need 1-2 days of training to master drone takeoff and landing, adjustment of spraying parameters (such as spray pressure), and daily maintenance of equipment. They also need basic smartphone operation skills. In addition to operators, there must be technical maintenance personnel. They are responsible for model updates, equipment troubleshooting (such as camera calibration and GPU driver maintenance), and data report interpretation. Farmland environments differ from test scenarios, with complex background conditions. Taking this farm as an example, under strong light, multispectral cameras are susceptible to reflection interference, which reduces detection accuracy. Thus, operations must be limited to before 9 a.m. or after 5 p.m. When air humidity exceeds 85%, drone battery life shortens, and ground equipment sensors are prone to moisture damage, requiring supporting waterproof enclosures. After implementing these measures, pesticide usage decreased from 180 L/ha to 99 compared to full-field spraying. Weeding effectiveness improved, increasing crop yield by 5%. Meanwhile, weed detection efficiency significantly enhanced, rising from 133 m\({}^{2}\)/h (manual inspection) to 5300 m\({}^{2}\)/h (drone inspection). Traditional chemical weed control requires approximately USD 30,000 annually for pesticide and labor costs. In smart agricultural farms, agricultural output increases by USD 5000, with annual equipment maintenance and pesticide expenses around USD 14,000. Compared with traditional methods, the annual gain is approximately USD 20,000. The initial equipment investment can be recovered within 2 to 3 years. Through this case, the implementation of smart agriculture can indeed promote the development of agricultural economy. For small- and medium-sized farms, priority should be given to lightweight solutions with low-cost initial equipment. They can realize basic links such as crop identification and disease monitoring. With the further improvement of algorithm models, data acquisition will also be simplified. This can further reduce operation difficulty and costs. For large-scale farms, appropriate increases in initial investment can be made. They can use hardware facilities such as soil sensors, weather stations, and high-performance edge devices. Combined with cloud computing platforms, they can conduct data integration and yield prediction. Supporting infrastructure should include high-precision positioning systems to support automatic driving of agricultural machinery.