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.
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}
}