Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 5 September 2024 (07:02:41 CEST)
How to cite:
Gourshettiwar, P.; Reddy, K. A Comprehensive Analysis with Machine Learning Algorithm and IoT Integration in Hydroponic Vegetable System for Nu-trition Management of Plants/Crops. Preprints2024, 2024090279. https://doi.org/10.20944/preprints202409.0279.v1
Gourshettiwar, P.; Reddy, K. A Comprehensive Analysis with Machine Learning Algorithm and IoT Integration in Hydroponic Vegetable System for Nu-trition Management of Plants/Crops. Preprints 2024, 2024090279. https://doi.org/10.20944/preprints202409.0279.v1
Gourshettiwar, P.; Reddy, K. A Comprehensive Analysis with Machine Learning Algorithm and IoT Integration in Hydroponic Vegetable System for Nu-trition Management of Plants/Crops. Preprints2024, 2024090279. https://doi.org/10.20944/preprints202409.0279.v1
APA Style
Gourshettiwar, P., & Reddy, K. (2024). A Comprehensive Analysis with Machine Learning Algorithm and IoT Integration in Hydroponic Vegetable System for Nu-trition Management of Plants/Crops. Preprints. https://doi.org/10.20944/preprints202409.0279.v1
Chicago/Turabian Style
Gourshettiwar, P. and KTV Reddy. 2024 "A Comprehensive Analysis with Machine Learning Algorithm and IoT Integration in Hydroponic Vegetable System for Nu-trition Management of Plants/Crops" Preprints. https://doi.org/10.20944/preprints202409.0279.v1
Abstract
The aim of this article is to discuss the transformative amalgamation of Internet of Things and Machine Learning technologies within the domain of vegetable hydroponic systems for nutrition management. Hydroponics, being an efficient cultivation method, benefits a great deal from the precision and adaptability that machine learning algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things. This study summarizes the latest research and underlines how ML and IoT may work together, focusing on nutrient optimization, plant development, and resource efficiency. The use of ML algorithms, the function of IoT devices for real-time monitoring, communication protocols, scalability issues, and implementation are among some of the key subjects of the discussion. The research indicates benefits to crop output, efficiency in using resources, and sustainability based on the case studies and the analysis of results. However, ethical problems and some complications concerning data privacy do call for responsible adoptions. The conclusion of this paper provides directions for further research and calls for more investigation into state-of-the-art machine learning approaches and scalable solutions for the re-silient and sustainable future of hydroponic agriculture. The machine learning and deep learning models introduced in this research were evaluated against contemporary studies, revealing an accuracy enhancement ranging from 1.17% to 5.25%, depending on the dataset and algorithm employed. The present study conducts a comparative analysis involving machine learning algo-rithms, indicating that among all the models, the Decision Tree and Gradient Boosting Classifier achieved an accuracy of 99.42% in the dataset by making stage-wise decisions.
Keywords
Hydoponics; Machine Learning; IoT
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
