Soil Characterization of Watermelon Field through Internet of Things: A New Approach to Soil Salinity Measurement

TL;DR

This paper presents an IoT-based system for real-time soil characterization in watermelon fields, measuring key parameters like moisture, temperature, pH, and salinity to optimize cultivation conditions and improve yield.

Contribution

It introduces a novel IoT framework integrating sensors, cloud data, and neural network analysis for precise soil salinity measurement in agriculture.

Findings

Sensor data closely matches laboratory soil measurements

The system enables real-time monitoring via mobile and web applications

Neural network model accurately predicts soil salinity from resistivity data

Abstract

In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).