Optimizing Soil Health Management in Smart Agriculture: Deep Learning Algorithms for Nutrient Analysis and Fertilizer Recommendation with Precision Agriculture Systems

Hengyi Zang; Xinqi Dong

doi:10.5281/zenodo.10444996

Authors

Hengyi Zang Universidad Tecnológico Universitam
Xinqi Dong University of Maine at Presque Isle

DOI:

https://doi.org/10.5281/zenodo.10444996

References:

19

Keywords:

Deep Learning Algorithms, Smart Agriculture, Sustainable Farming

Abstract

Today, technology makes the management of soil health the key to sustainable, high-yield agriculture. This article discusses a new approach using artificial intelligence and deep learning to understand the nutrients needed in the soil and provide fertilizer guidelines for advanced agriculture. We are using modern agricultural techniques, coupled with artificial intelligence, to develop a new way to protect soil that is more convenient, accurate and intelligent. Our study uses complex soil data to accurately predict soil water scarcity. We found a special algorithm. In addition, we have proposed an AI-driven fertilizer recommendation system that can customize different solutions for different soils. This research not only aligns AI with the practical needs of agriculture, but also creates new and more useful technologies for future smart agriculture innovations, promoting more advanced smart agriculture, fewer environmental risks, and smarter sustainable development.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Hengyi Zang, Universidad Tecnológico Universitam

Male, a phd student at Universidad Tecnológico Universitam.

Xinqi Dong, University of Maine at Presque Isle

Male, bachelor student at University of Maine at Presque Isle.

References

Qiao, Y., Ni, F., Xia, T., Chen, W., & Xiong, J. (2024, January). AUTOMATIC RECOGNITION OF STATIC PHENOMENA IN RETOUCHED IMAGES: A NOVEL APPROACH. In The 1st International scientific and practical conference “Advanced technologies for the implementation of new ideas”(January 09-12, 2024) Brussels, Belgium. International Science Group. 2024. 349 p. (p. 287).

Qiao, Y., Jin, J., Ni, F., Yu, J., & Chen, W. (2023). APPLICATION OF MACHINE LEARNING IN FINANCIAL RISK EARLY WARNING AND REGIONAL PREVENTION AND CONTROL: A SYSTEMATIC ANALYSIS BASED ON SHAP. WORLD TRENDS, REALITIES AND ACCOMPANYING PROBLEMS OF DEVELOPMENT, 331.

QIAO, Y., & NI, F. (2023). RESEARCH ON THE INTERDISCIPLINARY APPLICATION OF COMPUTER VISION TECHNOLOGY IN INTELLIGENT AGRICULTURAL MACHINERY. АКТУАЛЬНЫЕ ВОПРОСЫ ОБЩЕСТВА, НАУКИ И ОБРАЗОВАНИЯ 3, 34.

Kang, H., Ye, J., Wang, H., Dalir, H., & Sorger, V. J. (2023, October). Freespace Optical Interferometric Reconfigurable Complex Convolution Module. In Laser Science (pp. JTu4A-42). Optica Publishing Group.顶端

J. Ye, H. Kang, H. Wang, S. Altaleb, E. Heidari, N. Asadizanjani, V. J. Sorger, and H. Dalir, "OAM beams multiplexing and classification under atmospheric turbulence via Fourier convolutional neural network," in Frontiers in Optics + Laser Science 2023 (FiO, LS), Technical Digest Series (Optica Publishing Group, 2023), paper JTu4A.73.

J. Ye et al., "Multiplexed OAM beams classification via Fourier optical convolutional neural network," 2023 IEEE Photonics Conference (IPC), Orlando, FL, USA, 2023, pp. 1-2, doi: 10.1109/IPC57732.2023.10360629. keywords: {Multiplexing;Orbital calculations;Channel capacity;Optical computing;Optical fiber networks;Convolutional neural networks;Optical beams;optical communication;Multiplexed OAM beams;Fourier optical CNN},

Wang, X., Yang, Z., Ding, H., & Guan, Z. (2023). Analysis and prediction of UAV-assisted mobile edge computing systems. Mathematical Biosciences and Engineering, 20(12), 21267-21291.

Wang, X.; Yang, Z.; Ding, H. Application of Polling Scheduling in Mobile Edge Computing. Axioms 2023, 12, 709. https://doi.org/10.3390/axioms12070709

Zhang, Y., Gong, Y., Cui, D., Li, X., & Shen, X. (2024). DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans. arXiv preprint arXiv:2401.15354.

Gao, L., Cordova, G., Danielson, C., & Fierro, R. (2023, October). Autonomous Multi-Robot Servicing for Spacecraft Operation Extension. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 10729-10735). IEEE.

Zhang, Y., Wang, X., Gao, L., & Liu, Z. (2020). Manipulator Control System Based on Machine Vision. In International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence 7 (pp. 906-916). Springer International Publishing.

Song, Z., Liang, H., Ding, H., & Ma, M. (2023). Structure design and mechanical properties of a novel anti-collision system with negative Poisson's ratio core. International Journal of Mechanical Sciences, 239, 107864.

Song, Z., & Ding, H. (2023). Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions. Nonlinear Dynamics, 111(12), 11115-11134.

Ma, M., Zhou, X., Liu, Q., Wang, R., & Song, Z. (2021). Negative Compressibility in Hexagonal and Trigonal Models Constructed by Hinging Wine‐Rack Mechanism. physica status solidi (b), 258(5), 2000568.

Chen, S., Li, K., Fu, H., Wu, Y. C., & Huang, Y. (2023). Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic. Atmosphere, 14(6), 1023.

Liang, Y., Wang, X., Wu, Y. C., Fu, H., & Zhou, M. (2023). A Study on Blockchain Sandwich Attack Strategies Based on Mechanism Design Game Theory. Electronics, 12(21), 4417.

Ji, Y., Zhang, X., Wang, X., Huang, X., Huang, B., Zheng, J. H., & Li, Z. (2018, November). An equivalent modeling method for multi-port area load based on the extended generalized ZIP load model. In 2018 International Conference on Power System Technology (POWERCON) (pp. 553-558). IEEE.

Huang, B., Li, P., Zheng, J. H., & Wu, Q. H. (2018). A modified ward equivalent based on sensitivity matrices for static security analysis. IEEJ Transactions on Electrical and Electronic Engineering, 13(11), 1675-1676.

Huang, B., & Wang, J. (2023). Adaptive Static Equivalences for Active Distribution Networks With Massive Renewable Energy Integration: A Distributed Deep Reinforcement Learning Approach. IEEE Transactions on Network Science and Engineering.