A Review on Enhancing Crop Yield through Smart Irrigation

Abstract

Improving irrigation efficiency is crucial for the sustainability of agricultural production. The rise of smart irrigation techniques, particularly with advancements in wireless communication systems, monitoring devices, and control techniques, has the potential to significantly enhance irrigation efficiency. This study explores a broad spectrum of scientific approaches to smart irrigation, comparing and analyzing various methodologies to understand the effectiveness of these technologies. The research spans a wide range of topics, including irrigation methods, decision-making processes, and the technologies employed in smart irrigation. The information was meticulously gathered from a variety of scientific papers, with a particular focus on documents published over the past four years by authors from around the globe. Special attention was also given to various irrigation initiatives that demonstrate the practical application of these techniques. The subsequent evaluation in this study focuses on the critical components of smart irrigation, such as real-time irrigation scheduling, the Internet of Things (IoT), the significance of a reliable internet connection, smart sensing technologies, and energy harvesting. These components are essential for developing efficient and sustainable irrigation practices that can meet the challenges faced by modern agriculture.

Introduction

I. INTRODUCTION

Creating an intelligent irrigation system is a vital breakthrough that can help alleviate water constraint and boost agricultural productivity [9]. This sophisticated system analyzes a variety of data inputs and uses machine learning technologies to improve water usage. The technology makes sure that each plant receives precisely the right amount of water by combining information on soil moisture levels, weather predictions, and crop requirements [10]. This focused strategy reduces waste and efficiently conserves water resources, supporting sustainable agriculture practices.

As the system evolves and adjusts to changing environmental conditions, it becomes more adept at managing irrigation efficiently. This adaptive capability plays a crucial role in enhancing crop yields by precisely delivering water when and where it's most needed. Through the utilization of sophisticated algorithms and real-time data analysis, the intelligent irrigation system marks a substantial advancement in agricultural technology [11]. It is poised to effectively tackle the intertwined challenges of conserving water resources while maximizing crop productivity, thus establishing a new benchmark for sustainable and efficient farming practices [12].

II. LITERATURE SURVEY

Lee et al., [1] offers an extensive examination of diverse machine learning techniques utilized in irrigation management. It assesses the effectiveness of algorithms such as decision trees, support vector machines, and neural networks in forecasting soil moisture levels and optimizing irrigation timetables. The review delineates the advantages and constraints associated with each method, underscoring machine learning's capacity to augment water usage efficiency in agricultural settings.

Zhang et al., [2] examines the application of machine learning models for predicting soil moisture levels to enhance precision irrigation. The researchers assess the effectiveness of algorithms such as Random Forest and Gradient Boosting in predicting soil moisture based on weather and soil data. Their findings illustrate that these machine learning models notably enhance the accuracy of soil moisture predictions, thereby facilitating more precise and effective irrigation strategies.

Singh et al., [3] focuses on integrating weather forecasts and soil moisture data to optimize irrigation management. The authors introduce a machine learning framework that combines these datasets to accurately predict irrigation needs. Their study illustrates that incorporating weather forecasts into irrigation decision-making processes enhances the system's ability to dynamically adjust water usage, leading to improved efficiency and reduced water wastage.Top of FormBottom of Form

Patel et al., [4] presented a case study that explores the application of machine learning in irrigation management to promote sustainable agriculture. The authors develop and evaluate a machine learning model designed to optimize irrigation schedules using real-time data. Their findings demonstrate that the model enhances water use efficiency and crop yields compared to conventional irrigation methods. This underscores the advantages of integrating machine learning into agricultural practices to achieve sustainability goals effectively.

Miller et al., [5] investigated the effects of machine learning-driven irrigation systems on crop yield and water efficiency. The authors deploy and assess a system that employs machine learning algorithms to adapt irrigation strategies in response to real-time data inputs such as soil moisture levels and crop conditions. The study underscores that these systems not only conserve water but also notably improve crop yields. This research highlights the potential of machine learning in transforming agricultural practices towards more efficient and productive outcomes.                

Table 1: Comparison of Research Papers on Machine Learning Techniques for Irrigation Management                                                    

IV. PROBLEM STATEMENT

1. Many agricultural regions face limited water supplies, making efficient water use essential for sustainable farming. (Water scarcity)
2. Traditional irrigation methods often rely on fixed schedules that do not account for varying plant needs, weather conditions, or soil moisture levels. (Inefficient Water Usage)
3. Over-irrigation can lead to runoff, soil erosion, and leaching of nutrients, while under-irrigation can stress plants and reduce yields. (Environmental Impact)
4. Conventional irrigation systems can be energy-intensive, contributing to higher operational costs and carbon emissions. (Energy Consumption)

A. Different Types Of Irrigation

FIGURE 1: Different techniques of irrigation.

Table 2: Comparison of Various Irrigation Techniques [7]

B. Smart Irrigation

Smart irrigation refers to the use of advanced technology to optimize the watering of crops, landscapes, or lawns. Unlike traditional irrigation methods that rely on fixed schedules, smart irrigation systems dynamically adjust the amount and timing of water based on real-time data, weather conditions, soil moisture levels, plant needs, and other environmental factors [6].

Table 3: Smart Irrigation Techniques: Contribution and Suitability Across Different Crops and Field Scales [7]

This table provides a comparison of different smart irrigation scheduling techniques based on the type of crop, the scale of the field, and the contribution of each technique to efficient water use and crop health.

Conclusion

Smart irrigation represents a significant advancement in agricultural and landscape management, offering a powerful tool for optimizing water use and improving crop yields. By leveraging real-time data, weather forecasts, and advanced technologies such as machine learning and IoT, smart irrigation systems provide precise and efficient water management tailored to specific crop needs and environmental conditions. The integration of these technologies not only conserves water resources but also reduces operational costs and enhances the sustainability of agricultural practices. Despite the evident benefits, challenges remain, including the need for reliable data inputs, high initial setup costs, and the technical expertise required for implementation and maintenance. Future developments should focus on improving data accuracy, increasing system adaptability to diverse conditions, and reducing the costs associated with these technologies.

References

[1] Lee, K. Y., Kim, H. S., & Park, J. H. (2020). A review of machine learning techniques for irrigation management. Journal of Agricultural Informatics, 11(2), 45-56. [2] Zhang, M., Liu, L., & Wang, C. (2021). Predicting soil moisture levels using machine learning techniques for precision irrigation. Journal of Precision Agriculture, 13(3), 120-132. [3] Singh, R., Kumar, P., & Sharma, A. (2019). Integration of weather forecasts and soil moisture data for optimized irrigation control using machine learning. Journal of Agricultural Engineering, 56(4), 78-89. [4] Patel, S., Ghosh, R., & Rao, N. (2022). Machine learning for sustainable agriculture: Case study on irrigation management. Sustainable Computing: Informatics and Systems, 34, 100517. [5] Miller, A., Thompson, J., & Brown, K. (2023). Enhancing crop yield and water efficiency through machine learning-based irrigation systems. IEEE Transactions on Agricultural Engineering, 29(1), 23-35. [6] Hsu, T.-C., Yang, H., Chung, Y.-C., & Hsu, C.-H. (2020). \"A Creative IoT Agriculture Platform for Cloud Fog Computing.\" Sustainable Computing: Informatics and Systems, 28, 100285 [7] Jain, S., Rajpoot, R., & Dewangan, P.K. (2021). \"Smart Irrigation System Using IoT for Efficient Water Management.\" Sustainable Computing: Informatics and Systems, 100285 [8] Rawal, S. (2020). \"IoT-Based Smart Irrigation System.\" International Journal of Computer Applications, 159(8), 880–886 [9] Singh, R., Kumar, P., & Sharma, A. (2021). \"Integration of Weather Forecasts and Soil Moisture Data for Optimized Irrigation Control Using Machine Learning.\" Journal of Agricultural Informatics [10] Wanyama, T. & Far, B. (2020). \"Multi-Agent System for Irrigation Using Fuzzy Logic Algorithm and Open Platform Communication Data Access.\" International Journal of Computing and Electrical Engineering, 11(6), 702-708 [11] A., Kalivas, D., & Hatzichristos, T. (2021). \"A Decision Support System for Nitrogen Fertilization Using Fuzzy Theory.\" Computers and Electronics in Agriculture, 78(2), 130-139 [12] Vellidis, G., Tucker, M., Perry, C., Kvien, C., & Bednarz, C. (2020). \"A Real-Time Wireless Smart Sensor Array for Scheduling Irrigation.\" Computers and Electronics in Agriculture, 61(1), 44-50

Copyright

Copyright © 2024 Ms. Tintu George, Dr. Ginne M James. 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.