International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Design a Machine Learning - Based Crop Stress Detection and Water Management System |
|---|---|
| ABSTRACT | Agriculture plays a vital role in the economic development of many countries, particularly in regions where farming is the primary source of livelihood. However, traditional farming methods often rely on manual monitoring of environmental conditions such as soil moisture, temperature, and humidity. These methods are time-consuming, inefficient, and may lead to improper resource utilization. To address these challenges, this research proposes a Smart Farm system that integrates Artificial Intelligence, Internet of Things (IoT), and automation technologies for intelligent crop monitoring and irrigation control. The proposed system uses machine learning models to predict crop stress by analyzing environmental parameters including soil moisture levels, temperature, humidity, and light intensity. Data collected through sensors is processed using predictive algorithms to determine whether crops are experiencing stress conditions. Based on the prediction results, an automated irrigation system is triggered using an ESP32 microcontroller to supply water only when required. This approach improves water management, increases crop productivity, and reduces human intervention. Additionally, the system provides real-time monitoring and remote access to farmers through a cloud-based platform. Farmers can observe crop conditions, irrigation status, and environmental trends through a dashboard interface. The integration of AI-driven analytics with IoT-based hardware components enables efficient resource utilization and sustainable farming practices. The experimental results demonstrate that the proposed system effectively predicts crop stress and automates irrigation with high accuracy. This intelligent farming solution has the potential to transform traditional agriculture into data-driven precision farming. |
| AUTHOR | NANDINI.K, ARCHANA.S, JAYASRI.G, SARANYA.P Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404030 |
| pdf/30_To Design a Machine Learning - Based Crop Stress Detection and Water Management System.pdf | |
| KEYWORDS | |
| References | [1]. J. Smith, “IoT Based Smart Irrigation System for Agriculture,” IEEE Journal of Agricultural Technology, 2022. [2]. R. Kumar and P. Singh, “Machine Learning Approaches for Crop Health Monitoring,” IEEE Transactions on Smart Agriculture, 2023. [3]. S. Patel, “Precision Farming using IoT and AI Technologies,” International Journal of Agricultural Research, 2021. [4]. L. Wang et al., “Sensor Based Monitoring System for Smart Farming,” IEEE Sensors Journal, 2020. [5]. A. Sharma, “Artificial Intelligence Applications in Agriculture,” Springer Publications, 2022. [6]. K. Kumar and R. K. Yadav, “Maximizing Agricultural Water Efficiency: Integrating IoT and Supervised Learning for Smart Irrigation Optimization,” International Journal of Research – GRANTHAALAYAH, 2024. [7]. H.-R. Leao et al., “Artificial Intelligence-Powered IoT-Based Irrigation System for Precision Farming,” International Journal of Intelligent Systems and Applications in Engineering, 2024. [8]. P. Dhara shive and M. Saw ale, “Smart Irrigation System Techniques using Artificial Intelligence and IoT,” International Journal of Intelligent Systems and Applications in Engineering, 2024. [9]. M. Ramesh et al., “Smart Agriculture: IoT and Machine Learning for Crop Monitoring and Precision Farming,” International Journal of Intelligent Systems and Applications in Engineering, 2024. [10]. S. Sivakumar et al., “Sustainable Agriculture for Crop-Field Monitoring and Irrigation Automation Using Internet of Things (IoT),” International Journal of Engineering Research & Technology (IJERT), 2024. |
