Published on: April 2026
THE PLANT HEALTH MONITORING WEB APPLICATION USING MACHINE LEARNING
Sonali Chandrakant More Komal Gharat
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Abstract
Plant diseases and unfavorable environmental conditions pose significant challenges to agricultural productivity, often remaining undetected until severe damage occurs. This paper presents a full-stack web application designed to monitor plant health and provide intelligent crop recommendations based on environmental conditions such as temperature, humidity, sunlight, and watering frequency. The system utilizes a Python-based machine learning backend that trains and evaluates three supervised classification models: Decision Tree, Random Forest, and Logistic Regression. The best-performing model is selected and deployed for real-time prediction through a REST API. The frontend is implemented as a responsive multi-page web interface that enables users to perform plant recommendation, health prediction, and leaf image-based disease detection. Experimental results demonstrate that the Decision Tree model achieves the highest accuracy, making it suitable for deployment. The system offers a practical, accessible, and efficient solution for plant health monitoring and agricultural decision support.
How to Cite this Paper
More, S. C. & Gharat, K. (2026). The Plant Health Monitoring Web Application using Machine Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.476
More, Sonali, and Komal Gharat. "The Plant Health Monitoring Web Application using Machine Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.476.
More, Sonali, and Komal Gharat. "The Plant Health Monitoring Web Application using Machine Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.476.
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- •Published on: Apr 18 2026
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