Smart Irrigation System Using Soil Moisture Prediction with Deep CNN for Various Soil Types

Abstract

Soil moisture sensing plays a crucial role in agriculture as it directly impacts plant growth and can significantly enhance crop productivity. With the advent of technology, agriculture applications have undergone a revolution, enabling more advanced and efficient practices. One such advancement is the use of soil moisture sensors, which provide valuable information about the current water level of the soil, including whether it is dry, wet, or excessively saturated. These sensors have become indispensable tools for farmers and growers, empowering them to make informed decisions regarding irrigation schedules, water management strategies, and overall crop health. By accurately assessing soil moisture levels, farmers can optimize water usage, prevent water stress or overwatering, and promote healthier plant development, ultimately leading to improved yields and sustainability in agriculture. The objective of the proposed study is to investigate the effective soil moisture sensors by considering three sensors and an automated system for watering the soil for agriculture. A comparative analysis is performed for different commercial off-the-shelf soil moisture sensors in cost, accuracy, durability, and corrosion resistance. Secondly, this study further gives soil moisture reading as data input to the Convolutional Neural Network (CNN) to classify whether water is required or not for the soil at a particular temperature which would help to conserve water and develop agriculture. An automated system which can help the farmer for irrigation and cultivation of the soil obtained accuracy as 95.23%.

Received: 9 August 2023| Revised: 31 July 2024 | Accepted: 10 August 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Kaggle.com at https://www.kaggle.com/datasets/amirmohammdjalili/soil-moisture-dataset.

Author Contribution Statement

Sangeetha S. K. B: Conceptualization, Methodology, Software, Resources, Writing - original draft, Supervision. Rajeshwari Rajesh Immanue: Conceptualization, Methodology, Software, Resources, Writing - original draft, Supervision. Sandeep Kumar Mathivanan: Methodology, Investigation, Writing - review & editing, Visualization. Prabhu Jayagopal: Validation. Sukumar Rajendran: Data curation. Saurav Mallik: Data curation, Project administration Aimin Li: Data curation, Project administration.