Soil Moisture Prediction Using Machine Learning: A Comparative Study of Sensor Technologies and Environmental Factors

Abstract

Background / Purpose: A major worldwide challenge is the effective use of water resources in agriculture, especially when it comes to the production of deeply rooted crops like coconut, coffee, Cardamom and arecanut. Conventional irrigation techniques frequently use water inefficiently, which can lead to over-irrigation or water stress, which eventually reduces crop productivity and sustainability. In order to transform irrigation techniques through real-time data collecting and astute decision-making, this research focuses on integrating Time Domain Reflectometry (TDR) sensors for continuous and precise soil moisture monitoring. TDR technology offers a novel way to enable intelligent irrigation systems in agriculture because of its high precision and quick reaction time.

Objective: The main goal of this study is to evaluate the effectiveness of TDR sensors in tracking soil moisture in arecanut cultivation and investigate their potential integration with intelligent irrigation systems to improve water efficiency.

Design/Methodology/Approach: Over the course of 40 days, TDR sensors were tested in the field on several soil types, including clay, loamy, and sandy. Systems for drip and jet irrigation were both assessed. The study, which was backed by a review of the literature and an analysis of experimental data, addressed sensor installation, calibration, and performance tracking.

Findings/Result: TDR sensors outperformed other sensors in arecanut plantations with improved responsiveness and dependability, demonstrating around 98% accuracy. They are the best choice despite being somewhat pricey because to their long-term advantages in accurate watering and substantial water savings. For effective water management and increased crop output, TDR sensors should be installed in moisture-critical zones, making TDR the go-to option for environmentally friendly arecanut farming.

Originality/Value: The contents of the paper are original and have been developed based on insights gathered from secondary sources.

Paper Type: This paper is a conceptual research study that presents a comparative analysis based on secondary data sources.