Home / Current Issue / Paper 1716035
Software-Based Smart Irrigation with Plant Disease Prediction
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1716035
Abstract
The core objective of this work is to empower farmers with smarter decision-making tools through a technology-driven irrigation platform built entirely on software. By applying image analysis techniques to user-submitted photographs, the system can recognise plant health issues and generate actionable guidance. Alongside disease identification, the platform evaluates key environmental variables — including temperature, relative humidity, and precipitation levels — to determine whether crop watering is warranted at any given time.Beyond water management, the system advises farmers on which crop varieties are most suitable for their conditions. It also serves as an early-warning mechanism, flagging potential crop health risks before visible damage occurs, enabling timely preventive intervention. Routine agricultural choice— such as deciding whether to water a specific field on a given day — are automated through data-driven computation, reducing the need for continuous hands-on monitoring. The platform is built with accessibility at its core: farmers can submit images from any standard computer and receive results within moments of uploading. The solution further enhances farm efficiency by supporting well-informed water management choices that conserve resources and strengthen overall irrigation performance. Taken together, this system advances precision agriculture by unifying several intelligent features into a single, cohesive software solution.
Keywords
Smart Irrigation System, Image Processing Technology, Plant Disease Detection, Soil Analysis, Agriculture.
References
[1] A. Kaur et al., “Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review,” IEEE Access, vol. 12, pp. 193902– 193922, 2024, 10.1109/ACCESS.2024.3510456.
[2] A. Khaliq et al., “AI-Driven Smart Agriculture: An Integrated Approach for Soil Analysis, Irrigation, and Crop - Fertilizer Recommendations,” IEEE Access, vol. 13, pp. 141124–141138, 2025, 10.1109/ACCESS.2025.3594162.
[3] W. Xiao and J. Hu, “Analyzing Effective Factors of Online Learning Performance by Interpreting Machine Learning Models,” IEEE Access, vol. 11, pp. 132435– 132447, 2023, 10.1109/ACCESS.2023.3334915.
[4] J.-J. Liu, H. Wu, and I. Riaz, “Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review,” IEEE Access, vol. 13, pp. 139766–139790, 2025, 10.1109/ACCESS.2025.3594361.
[5] A. AlZubi and K. Galyna, “Artificial Intelligence and Internet of Things for Sustainable Farming and Smart Agriculture,” IEEE Access, vol. 11, pp. 78686–78692, 2023,
[6] M. Hussain, “YOLOv1 to v8: Unveiling each variant—A comprehensive review of YOLO,” IEEE Access, vol. 12, pp. 42816–42833, 2024,
[7] A. Setyanto, T. B. Sasongko, M. A. Fikri, and I. K. Kim, “Near-edge computing aware object detection: A review,” IEEE Access, vol. 12, pp. 2989–3011, 2024, 10.1109/ACCESS.2023.3347548.
[8] M. Muzammul, A. Algarni, Y. Y. Ghadi, and M. Assam, “Enhancing UAV aerial image analysis: Integrating advanced SAHI techniques with realtime detection models on the VisDrone dataset,” IEEE Access, vol. 12, pp. 21621– 21633, 2024, 10.1109/ACCESS.2024.3363413.
[9] D. Thakur and V. Kumar, “FruitVision: Dual- attention embedded AI system for precise apple counting using edge computing,” IEEE Transactions on AgriFood Electronics, vol. 2, no. 2, pp. 445–459, Sept. 2024, 10.1109/TAFE.2024.3416221.
[10] J. Kaur and W. Singh, “A systematic review of object detection from images using deep learning,” Multimedia Tools and Applications, vol. 83, no. 4, pp. 12253–12338, Jan. 2024.
How to cite this paper
@article{1716035,
author = {Mahalakshmi N, Amirthasri M, Divya S},
title = {Software-Based Smart Irrigation with Plant Disease Prediction},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {709-715},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716035.pdf},
abstract = {The core objective of this work is to empower farmers with smarter decision-making tools through a technology-driven irrigation platform built entirely on software. By applying image analysis techniques to user-submitted photographs, the system can recognise plant health issues and generate actionable guidance. Alongside disease identification, the platform evaluates key environmental variables — including temperature, relative humidity, and precipitation levels — to determine whether crop watering is warranted at any given time.Beyond water management, the system advises farmers on which crop varieties are most suitable for their conditions. It also serves as an early-warning mechanism, flagging potential crop health risks before visible damage occurs, enabling timely preventive intervention. Routine agricultural choice— such as deciding whether to water a specific field on a given day — are automated through data-driven computation, reducing the need for continuous hands-on monitoring. The platform is built with accessibility at its core: farmers can submit images from any standard computer and receive results within moments of uploading. The solution further enhances farm efficiency by supporting well-informed water management choices that conserve resources and strengthen overall irrigation performance. Taken together, this system advances precision agriculture by unifying several intelligent features into a single, cohesive software solution.},
keywords = {Smart Irrigation System, Image Processing Technology, Plant Disease Detection, Soil Analysis, Agriculture.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716035}
}