Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
- Title
- Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
- Creator
- Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
- Description
- This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 231-240.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- ANN; CNN; Machine Learning; Precision Agriculture; Smart Irrigation; SVM
- Coverage
- Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Hasan D.S., Department of Computer Science and Information Technology, University of Salahaddin-Erbil, Erbil, Iraq; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303164846-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K., “Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 28, 2025, https://archives.christuniversity.in/items/show/19318.