Precision Sugarcane Monitoring Using SVM Classifier

TL;DR

This paper introduces a wireless sugarcane monitoring system that uses SVM and KNN clustering to detect infections from images, achieving 96% accuracy in identifying crop health issues.

Contribution

The study presents a novel integrated system combining environmental parameter monitoring with image-based infection detection using SVM and KNN clustering.

Findings

Achieved 96% accuracy in infection detection.

Successfully monitored crop health parameters wirelessly.

Demonstrated system effectiveness in real field conditions.

Abstract

India is agriculture based economy and sugarcane is one of the major crops produced in northern India. Productivity of sugarcane decreases due to inappropriate soil conditions and infections caused by various types of diseases , timely and accurate disease diagnosis, plays an important role towards optimizing crop yield. This paper presents a system model for monitoring of sugarcane crop, the proposed model continuously monitor parameters (temperature, humidity and moisture) responsible for healthy growth of the crop in addition KNN clustering along with SVM classifier is utilized for infection identification if any through images obtained at regular intervals. The data has been transmitted wirelessly from the site to the control unit. Model achieves an accuracy of 96% on a sample of 200 images, the model was tested at Lolai, near Malhaur, Gomti Nagar Extension.