Planned intervention: On Wednesday April 3rd 05:30 UTC Zenodo will be unavailable for up to 2-10 minutes to perform a storage cluster upgrade.
Published March 30, 2021 | Version v1
Journal article Open

Arduino Based Machine Learning and IoT Smart Irrigation System

  • 1. , Researcher, Robotics, Artificial Intelligence, IoT, USA
  • 2. Professor, Don Bosco Institute of Technology, Bangalore, India.
  • 1. Publisher

Description

We all depend on farmers in today's world. But is anybody aware of who the farmers rely on? They don't suffer from various irrigation issues, such as over-irrigation, under irrigation, underwater depletion, floods, etc. We are trying to build a project to solve some of the problems that will help farmers overcome the challenges. Owing to inadequate distribution or lack of control, irrigation happens because of waste water, chemicals, which can contribute to water contamination. Under irrigation, only enough water is provided to the plant, which gives low soil salinity, leading to increased soil salinity with a consequent build-up of toxic salts in areas with high evaporation on the soil surface. This requires either leaching to remove these salts or a drainage system to remove the salts. We have developed a project using IoT (Internet of Things) and ML to solve these irrigation problems (machine learning). The hardware consists of different sensors, such as the temperature sensor, the humidity sensor, the pH sensor, the raspberry pi or Arduino module controlled pressure sensor and the bolt IOT module. Our temperature sensor will predict the area's weather condition, through which farmers will make less use of field water. At a regular interval, our pH sensor can sense the pH of the soil and predict whether or not this soil needs more water. Our main aim is to automatically build an irrigation system and to conserve water for future purposes.

Files

D34810310421.pdf

Files (315.1 kB)

Name Size Download all
md5:7098c5125ae17fb4f7dd9db212e9c34e
315.1 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2231-2307 (ISSN)

Subjects

ISSN
2231-2307
Retrieval Number
100.1/ijsce.D34810310421