Banana Irrigation System and Scheduling based on Reinforcement Learning

Banana Irrigation System and Scheduling based on Reinforcement Learning

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Angelin Blessy, Avneesh Kumar, Prashant Johri
DOI : 10.14445/22315381/IJETT-V70I8P240

How to Cite?

Angelin Blessy, Avneesh Kumar, Prashant Johri, "Banana Irrigation System and Scheduling based on Reinforcement Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 394-400, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P240

Abstract
Water optimization and scheduling are essential for today's agriculture sector because water and energy usage are not adequately estimated. A tremendous amount of water is wasted in the irrigated fields. The combination of today's technologies provides the solution for managing water and providing the proper irrigation schedule. The Internet of Things and machine learning techniques are effectively used for smart agricultural fields. This paper proposes an effective water optimization and scheduling method that uses IoT components, the KNN algorithm, reinforcement learning, and person correlation techniques. The IoT components are used to collect the current requirements and predict the environmental status of the cultivation files. And is also used to transfer the information from the entire cultivation field to control fields. The KNN algorithm captures the nearest features from the cultivation fields. Environmental prediction, awards, or requirements of specific plants are performed using IoT and KNN capabilities. In this work, we applied a smart irrigation system used in banana cultivation. Based on the current prediction, the future requirements of water are calculated in a 12- hour time interval from 7 pm to 7 am, and it is calculated for up to 4 days. Compared to traditional cultivation, this proposed method reduces water usage by up to 24% of the water required.

Keywords
Smart Irrigation System, Scheduling, KNN, Reinforcement Learning, IoT, Banana Cultivation.

Reference
[1] [Online]. Available: https://www.avkindia.com/en/irrigation/types-of-irrigation
[2] [Online]. Available: http://www.agritech.tnau.ac.in/expert_system/banana/irrigationmanagement.html#:~:text=The%20total%20water%20requirement%20of ,as%20well%20as%20supplementary%20irrigation.
[3] Sun, Ziheng, and Liping Di, "A Review of Smart Irrigation Decision Support Systems," 9th International Conference on Agro-Geoinformatics Agro-Geoinformatics, IEEE, 2021.
[4] Gandhi, Ratnik, "Deep Reinforcement Learning for Agriculture: Principles and Use Cases," Data Science in Agriculture and Natural Resource Management, Springer, Singapore, pp. 75-94, 2022.
[5] Vianny, D. Maria Manuel, et al., "Water Optimization Technique for Precision Irrigation System using Iot and Machine Learning," Sustainable Energy Technologies and Assessments, vol. 52, pp. 102307, 2022.
[6] Ding, Xianzhong, and Wan Du, "Smart Irrigation Control Using Deep Reinforcement Learning," ACM/IEEE IPSN, 2022.
[7] Bu, Fanyu, and Xin Wang, "A Smart Agriculture Iot System Based on Deep Reinforcement Learning," Future Generation Computer Systems, vol. 99, pp. 500-507, 2019.
[8] Campoverde, Luis Miguel Samaniego, Mauro Tropea, and Floriano De Rango, "An IoT Based Smart Irrigation Management System using Reinforcement Learning Modeled through a Markov Decision Process," IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications DS-RT, IEEE, 2021.
[9] Chen, Mengting, et al., "A Reinforcement Learning Approach to Irrigation Decision-Making for Rice using Weather Forecasts," Agricultural Water Management, vol. 250, pp. 106838, 2021.
[10] Zhou, Ni, "Intelligent Control of Agricultural Irrigation Based on Reinforcement Learning," Journal of physics: Conference Series, IOP Publishing, vol. 1601, no. 5, 2020.
[11] Campoverde, Luis Miguel Samaniego, and Nunzia Palmieri, "A Reinforcement Learning Approach for Smart Irrigation Systems," Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, SPIE, vol. 12114, 2022.
[12] Yang, Yanxiang, et al., "Deep Reinforcement Learning-Based Irrigation Scheduling," Transactions of the ASABE, vol. 63, no. 3, pp. 549-556, 2020.
[13] Hung, Fengwei, and YC Ethan Yang. "Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach," Water Resources Research, vol. 57, no. 9, pp. e2020WR029262, 2021.
[14] Ding, Xianzhong, and Wan Du. "DRLIC: Deep Reinforcement Learning for Irrigation Control," ACM/IEEE IPSN, 2022.
[15] Binas, Jonathan, Leonie Luginbuehl, and Yoshua Bengio, "Reinforcement Learning for Sustainable Agriculture," ICML 2019 Workshop Climate Change: How Can AI Help, 2019.
[16] Overweg, Hiske, Herman NC Berghuijs, and Ioannis N. Athanasiadis, "CropGym: A Reinforcement Learning Environment for Crop Management," arXiv preprint arXiv:2104.04326, 2021.
[17] Sun, Lijia, et al., "Reinforcement Learning Control for Water-Efficient Agricultural Irrigation," 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), IEEE, 2017.
[18] Alibabaei, Khadijeh, et al., "Irrigation Optimization with a Deep Reinforcement Learning Model: Case Study on a Site in Portugal," Agricultural Water Management, vol. 263, pp. 107480, 2022.
[19] Din, Ahmad, et al., "A Deep Reinforcement Learning-Based Multi-Agent Area Coverage Control for Smart Agriculture," Computers and Electrical Engineering, vol. 101, pp. 108089, 2022.
[20] Perolat, Julien, et al. "A Multi-Agent Reinforcement Learning Model of Common-Pool Resource Appropriation," Advances in Neural Information Processing Systems, vol. 30, 2017.
[21] Sirisha, V. V. S. S., and G. Sahitya, "Smart Irrigation System for the Reinforcement of Precision Agriculture Using Prediction Algorithm: SVR Based Smart Irrigation," 2021 6th International Conference On Inventive Computation Technologies (ICICT), IEEE, 2021.
[22] Anthoniraj, S., P. Karthikeyan, and V. Vivek, "Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network," Journal of Mobile Multimedia, pp. 275-292, 2021.
[23] Meghashree V, Namratha Ganesh, Namratha Gopal, Aruna Rao BP, "Smart Village," International Journal of Electronics and Communication Engineering, vol. 7, no. 7, pp. 4-13, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I7P102
[24] Dhana Lakshmi. N and Gomathi K.S, "Smart Irrigation System Autonomous Monitoring and Controlling of Water Pump by Using Photovoltaic Energy," International Journal of Electronics and Communication Engineering, vol. 2, no. 11, pp. 21- 26, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I11P105
[25] Gang Xu, et al. "Improvement of Wireless Sensor Networks Against Service Attacks Based on Machine Learning." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 74-79. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P20.