Internet of Things and Machine Learning based Intelligent Irrigation System for Agriculture

Alanya-Arango J.; Alanya-Beltran J.; Ravichandran S.K.; Singh B.; Sasi A.; Gangodkar D. · Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022 (2022) · DOI: 10.1109/ic3i56241.2022.10073254

Resumen

Irrigated agriculture methods need a significant volume of water, and causes water waste. It is critically necessary to install an efficient watering system and lessen the volume of water wasted on this tiresome chore. It is a huge benefit of the computer vision (ML) - the Internet of Everything (Ot) era to construct expert machines that carry out this work successfully with little human endeavour. This work suggests an Embedded device Fluid ounces suggestion method for efficient water use with little farm involvement. In the agricultural field, IoT sensors are set up to capture important atmospheric and surface data. The obtained information is sent to and stored on a cloud-based server, where machine learning techniques are used to evaluate the information and recommend treatment to the farmers. This recommender system has an internal development process that makes the solution resilient and flexible. The test demonstrates that the suggested method operates admirably on the agricultural dataset from the National Institutes of Technology (Kit) Bhubaneswar as well as the information that we obtained.

Instituciones: Universidad Tecnológica del Perú