Intelligent Precision Farming with an Eco-Conscious Smart Irrigation System

Abstract

India’s agricultural sector plays a pivotal role in supporting the livelihoods of over half of the country’s population. However, with the rapidly growing demand for food, this sector is under immense pressure to ensure sustainability while enhancing productivity. Agriculture accounts for 83% of the nation’s water consumption, yet a significant portion of this water is wasted due to outdated and inefficient irrigation practices. This research introduces an advanced smart irrigation system utilizing Internet of Things (IoT) technology to enhance irrigation efficiency and ensure optimal water utilization. This system is designed around the Node MCU microcontroller and integrates advanced sensors, including soil moisture, temperature, and humidity sensors, to enable real-time monitoring and automated decision-making. This approach integrates advanced technology with sustainable methods, promoting water conservation while improving crop productivity and establishing a standard for contemporary agricultural practices.

Introduction

I. INTRODUCTION

Agriculture remains a cornerstone of India’s economy, employing nearly half of the population and contributing approximately 20% to the nation’s GDP. Despite advancements in irrigation infrastructure, traditional methods like flood and canal irrigation continue to dominate agricultural practices.

Such methods often lack efficiency, with substantial water losses resulting from evaporation, seepage, and over-irrigation. Research indicates that nearly 50% of the water utilized for agricultural purposes is wasted due to these inefficiencies, exacerbating the challenges posed by limited water resources.

The emergence of IoT technology has revolutionized the potential for addressing these inefficiencies. IoT-enabled systems facilitate automation, precise control, and real-time data collection, enabling farmers to make informed decisions about irrigation. This study introduces an IoT-based smart system for  irrigation that aims to reduce water wastage, enhance crop productivity, and alleviate the workload of farmers. By integrating advanced hardware and software, the system ensures optimal water usage, making it a sustainable and scalable solution for contemporary agriculture.

II. LITERATURE REVIEW

The field of smart irrigation has witnessed significant developments, yet many existing systems face limitations. Conventional systems often rely on single-parameter monitoring, which restricts their adaptability to varying environmental conditions. For instance, Djelout and Di (2021) developed an irrigation system that employs soil moisture sensors to initiate watering. However, such systems can be inaccurate as they do not consider external factors such as rainfall or uneven irrigation. Similarly, Keswani et al. (2019) proposed weather-dependent systems that adjust irrigation schedules based on regional forecasts. While effective in some scenarios, their reliance on unpredictable weather conditions reduces their reliability.

Programmable irrigation systems, like those developed by Khan et al. (2020), tailored irrigation schedules to specific terrains. However, these systems struggled with issues such as unequal water distribution and soil erosion, which undermined their efficiency. Despite addressing some challenges, these approaches lacked integration of multiple environmental parameters and failed to provide real-time, cloud-based monitoring for continuous improvement.

By combining soil, weather, and environmental data, the IoT-based system presented this study aims to overcome these limitations, offering a comprehensive and adaptive solution.

III. OBJECTIVES

This research aims to address critical inefficiencies in traditional irrigation practices and provide a comprehensive solution that optimizes resource utilization. The primary objective is to develop a system that minimizes water wastage by leveraging real-time soil and weather data. By maintaining optimal soil moisture and temperature levels, the system aims to significantly enhance crop productivity. Additionally, the research focuses on creating an automated solution that is scalable across diverse terrains and adaptable to various crop types. Through cloud integration, the system enables farmers to remotely monitor and manage irrigation processes via user-friendly mobile applications. These objectives align with the broader goals of achieving sustainability and increasing agricultural efficiency.

IV. METHODOLOGY

The proposed smart irrigation system integrates a robust combination of hardware and software to deliver an automated and efficient solution. Central to the system lies the Node MCU microcontroller, a cost-effective, Wi-Fi-enabled platform capable of handling real-time data collection and processing. The hardware setup includes a suite of sensors designed to monitor various environmental parameters. Soil moisture sensors measure the moisture content in the soil, triggering irrigation when levels fall below predefined thresholds. To further enhance precision, DHT11 sensors capture temperature and humidity data, enabling weather-based irrigation adjustments. Rain gauges are incorporated to detect rainfall and prevent unnecessary watering during wet conditions.

The system’s functionality is controlled by relay modules that act as actuators, operating water pumps based on sensor inputs. This automation ensures that irrigation is applied only when necessary, conserving water and maintaining optimal soil conditions. The software component is developed using Arduino IDE, which compiles and uploads the

control logic to the microcontroller. Additionally, the system integrates with Adafruit IO, a cloud-based platform that provides real-time data visualization and remote access. Farmers can monitor soil and weather conditions, adjust irrigation settings, and receive alerts through an intuitive interface.

Fig.1.Work flow Diagram

The workflow begins with the sensors collecting data on soil moisture, temperature, and humidity. The microcontroller processes this data and compares it to predefined thresholds to determine whether irrigation is required. If necessary, the water pump is activated, and the system updates Adafruit IO with real-time data for monitoring and analysis. By combining automation with real-time monitoring, the system ensures efficient water usage and optimal crop health.

Fig.2. Block Diagram

V. IMPLEMENTATION

To assess the effectiveness of the proposed system, experiments were carried out in two distinct agricultural zones. Zone A utilized traditional irrigation methods, while Zone B implemented the IoT-enabled smart irrigation system. The results highlighted the significant advantages of the IoT system. Zone B demonstrated a 70% reduction in water consumption compared to Zone A, showcasing the system’s ability to conserve vital resources. Furthermore, precise irrigation scheduling in Zone B led to a 30% increase in crop yield, underscoring the system’s effectiveness in enhancing productivity. Automation eliminated the requirement for manual involvement, allowing farmers in Zone B to focus on other essential activities, thereby improving operational efficiency. These results validate the potential of IoT-based solutions to transform traditional agricultural practices.

Fig.3.User Sign-Up Page

Fig.4.Hardware Model

VI. RESULTS AND DISCUSSION

The results of the study clearly illustrate the advantages of the IoT-based irrigation system over conventional methods. Conventional irrigation techniques often led to considerable water wastage, with approximately 100,000 liters of water being used per acre. In contrast, the IoT system reduced water consumption to around 30,000 liters per acre by ensuring Irrigation was implemented only when required. This effective water management not only conserved resources but also improved plant health and growth by maintaining optimal soil moisture levels. As a result, crop productivity increased by 35%, even in semi-arid regions with irregular rainfall patterns.

The system’s automation capabilities significantly reduced human labor, eliminating the need for constant monitoring and manual adjustments. Additionally, water wastage was reduced to just 5%, compared to the 50% wastage observed with traditional methods. By conserving water, maintaining soil health, and minimizing the risk of land degradation and salinity, the system promotes environmental sustainability. These findings underscore the transformative potential of IoT-based systems in modern agriculture, making them an indispensable tool for resource-efficient farming.

Conclusion

This study presents a novel IoT-based smart irrigation system that addresses the limitations of traditional irrigation practices. By integrating automation and real-time monitoring, the system achieves significant water conservation, enhances crop productivity, and reduces labour requirements. Continuous monitoring of soil moisture, temperature, and weather conditions guarantees that crops get the exact amount of water required, optimizing resource usage and minimizing waste. Automation streamlines irrigation processes, freeing farmers to focus on other critical tasks while improving overall operational efficiency. This IoT-driven approach provides a cost-effective and sustainable solution to the challenges of modern agriculture, making it a crucial tool for enhancing productivity and ensuring environmental sustainability.

References

[1] Divya J., et al., \"IoT-Based Crop-Field Monitoring and Irrigation Automation,\" IJARCCE, 2020. [2] Keswani, B., \"IoT-enabled Precision Agriculture Mechanisms,\" Neural Computation Applications, 2019. [3] Djelout K., et al., \"Soil Moisture Pattern Analysis for Crop Irrigation,\" Journal of Computer Science & IT, 2021. [4] Banumathi, P., \"IoT-Enhanced Bayesian Systems for Irrigation,\" IJSRCCE, 2017. [5] Osorio, M., \"Smart Irrigation Systems in Agriculture: A Systematic Review,\" Agronomy, 2023.

Copyright

Copyright © 2024 Tejas V, Vishaal Kumaar, Sudeep G, Srikanth S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.