A deep learning-based algorithm for crop Disease identification positioning using computer vision

Guoqing Cai; Jili Qian; Tianbo Song; Quan Zhang; Beichang Liu

doi:10.62051/ijcsit.v1n1.12

Authors

Guoqing Cai
Jili Qian
Tianbo Song
Quan Zhang
Beichang Liu

DOI:

https://doi.org/10.62051/ijcsit.v1n1.12

Keywords:

Computer vision positioning, Crop damage, Deep learning, Intelligent pest control

Abstract

Food security is fundamental to a country. As the main risk factors, pests and diseases seriously restrict the normal growth of crops and the quality and safety of agricultural products. With the intensification of climate change and the continuous adjustment of farming methods, crop diseases and pests have become more frequent in recent years. Therefore, the agricultural production mode has gradually moved from family production to large-scale agricultural planting, and the production equipment has become more automated and intelligent. Agricultural intelligent robots can reduce labor costs in the process of agricultural production and improve the standardization of agricultural production. The application of computer vision in agriculture is rapidly becoming an important aspect of modern agricultural technology, especially in crop positioning and management. Through the use of advanced image processing algorithms and pattern recognition technology, computer vision systems are able to accurately identify and locate various crops in the field, enabling automated and precise management. This technology shows great potential for crop health monitoring, pest identification, and maturity assessment. For example, by analyzing images of plants, computer vision systems can spot signs of lesions or nutrient deficiencies in time and guide farmers to treat them accordingly. In addition, this technology can also be used to guide automated agricultural machinery, such as driverless tractors and harvesters, to improve the efficiency of crop harvesting and reduce labor costs. In general, the combination of computer vision and crops provides new technical means for the development of modern precision agriculture, which helps to improve the efficiency and sustainability of agricultural production.

Downloads

Download data is not yet available.

References

CAMARGO A, SMITH J S. lmage pattern classification for the identification of diseasecausing agents in plants[J]. Computers and Electronics in Agriculture, 2009, 66 (2): 121-125.

Xinyu Zhao, et al. “Effective Combination of 3D-DenseNet’s Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 81-84, https://doi.org/10.54097/iMKyFavE.

Shulin Li, et al. “Application Analysis of AI Technology Combined WithSpiral CT Scanning in Early Lung Cancer Screening”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 52-55, https://doi.org/10.54097/LAwfJzEA.

Liu, Bo & Zhao, Xinyu & Hu, Hao & Lin, Qunwei & Huang, Jiaxin. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science. 3. 36-42. 10.53469/jtpes.2023.03(12).06.

Yu, Liqiang, et al. “Research on Machine Learning With Algorithms and Development”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 7-14, doi:10.53469/jtpes.2023.03(12).02.

Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.0

Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.6.

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 127-131. https://doi.org/10.54097/zJ4MnbWW.

Zhang, Q., Cai, G., Cai, M., Qian, J., & Song, T. (2023). Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems, 6(1), 99-102.3

Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. (2023). Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. Frontiers in Computing and Intelligent Systems, 6(1), 66-69.7

Wan,Weixiang, et al. "Development and Evaluation of Intelligent Medical Decision Support Systems." Academic Journal of Science and Technology 8.2 (2023): 22-25.

Tian, M., Shen, Z., Wu, X., Wei, K., & Liu, Y. (2023). The Application of Artificial Intelligence in Medical Diagnostics: A New Frontier. Academic Journal of Science and Technology, 8(2), 57-61.7

Shen, Z., Wei, K., Zang, H., Li, L., & Wang, G. (2023). The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data. Academic Journal of Science and Technology, 8(3), 132-135.2

Zheng He, et al. “The Importance of AI Algorithm Combined With Tunable LCST Smart Polymers in Biomedical Applications”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 92-95, https://doi.org/10.54097/d30EoLHw.

Prediction of Atmospheric Carbon Dioxide Radiative Transfer Model based on Machine Learning. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 132-136. https://doi.org/10.54097/ObMPjw5n

[18] Liu, Y., Duan, S., Shen, Z., He, Z., & Li, L. (2023). Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology. Journal of Theory and Practice of Engineering Science, 3(12), 22-28.1

Xinyu Zhao, et al. “Effective Combination of 3D-DenseNet’s Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 81-84, https://doi.org/10.54097/iMKyFavE.

Liu, B. (2023). Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning. International Journal of Computer Science and Information Technology, 1(1), 17-23.

Pan, Linying, et al. “Research Progress of Diabetic Disease Prediction Model in Deep Learning”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 15-21, doi:10.53469/jtpes.2023.03(12).03.

K. Tan and W. Li, "Imaging and Parameter Estimating for Fast Moving Targets in Airborne SAR," in IEEE Transactions on Computational Imaging, vol. 3, no. 1, pp. 126-140, March 2017, doi: 10.1109/TCI.2016.2634421.

Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.

Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach. arXiv preprint arXiv:2401.00534.

Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation. 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC), 1–7. doi:10.1109/ICAIC57335.2023.10044182

Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. 2022 IEEE Ninth International Conference on Communications and Networking (ComNet), 1–7. doi:10.1109/ComNet55492.2022.9998475.

Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 1–7. doi:10.23919/SoftCOM52868.2021.9559076.