IoT-Based System for Detecting Grain Moisture Content to Improve Rice Harvest Quality

Authors

  • Eka Ismantohadi Informatics Department, Politeknik Negeri Indramayu
  • Fachrul Pralienka Bani Muhamad Informatics Department, Politeknik Negeri Indramayu
  • Fauzan Islakhuddin Informatics Department, Politeknik Negeri Indramayu
  • Budi Warsito Information System Department, Universitas Diponegoro
  • Oky Dwi Nurhayati Information System Department, Universitas Diponegoro
  • Ade Alvi Informatics Department, Politeknik Negeri Indramayu
  • Ikhwan Maulana Wachid Informatics Department, Politeknik Negeri Indramayu

DOI:

https://doi.org/10.35842/icostec.v3i1.73

Keywords:

BME280 Sensor, Capacitive Soil Moisture Sensor, ESP32 module, Grain Moisture Content, IoT-based System, Negative Temperature Coefficient (NTC) sensors

Abstract

Drying grains serves to remove excess moisture,
prevent rot, and increase shelf life for farmers. The duration of
time required to dry rice is a critical parameter affecting grain
quality and sale value. To achieve ideal grain moisture content,
various farmer groups utilize grain drying machines such as Bed
Dryers. However, drying machines are currently unable to
automatically learn the characteristics of the moisture content of
the grain being dried, necessitating human input to determine the
optimum moisture content. To address this challenge, we propose
the creation of an Internet of Things (IoT)-based grain moisture
content measuring device, which can be integrated into a Bed
Dryer machine. The resulting IoT tool can be augmented with
machine learning, web, or mobile applications. However, this
research is solely focused on IoT tools design and manufacturing.
The ESP32 module functions as a data control and communication
device for each sensor connected to the internet network in IoT
devices. The sensors utilized for measuring grain moisture content
are BME280, Capacitive Soil Moisture, and Negative
Temperature Coefficient (NTC) sensors. The findings present
data on grain temperature values, grain drying environmental
temperatures, and grain moisture content. This information can
serve as a reference point for developing machine learning
algorithms, web applications, and mobile applications that guide
the ideal water content value of grains.

Published

2024-02-17