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Related Experiment Video

Updated: Jul 15, 2026

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

Precise and Quantitative Chlorosis Severity Assessment Framework (PQCSAF) using evolutionary superpixels.

Sourav Samanta¹, Sanjoy Pratihar², Sanjay Chatterji²

¹Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani, Kalyani, West Bengal, 741235, India. sourav.uit@gmail.com.

Scientific Reports

|October 24, 2025

Summary

This study introduces a novel computer-vision approach for accurately detecting plant disease severity, specifically chlorosis, by analyzing leaf yellowness. The method enhances smart agriculture by enabling precise disease diagnosis on edge devices.

Keywords:

Chlorosis disease Disease detection Disease severity index Evolutionary feature selection Multi swarm Cuckoo search Plant pathology Pongamia pinnata Superpixel

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Area of Science:

Computer Vision in Agriculture
Plant Pathology
Artificial Intelligence for Crop Monitoring

Background:

Smart agriculture demands automated plant disease recognition systems.
Accurate estimation of disease severity, like chlorosis (leaf yellowing), is challenging.
Computer vision offers promising solutions for automated plant disease identification.

Purpose of the Study:

To develop a novel, highly accurate computer-vision approach for detecting disease-affected areas and estimating chlorosis severity.
To improve precision in plant disease diagnosis for smart agriculture applications.

Main Methods:

An evolutionary superpixel-based method was used for grouping leaf color patches.
Color-GLCM techniques extracted texture features for yellowness detection.
A multi-swarm Cuckoo search optimized feature selection, followed by classification using Decision Tree, KNN, SVM, and MLP.

Main Results:

The proposed PQCSAF system achieved high classification accuracies for four chlorosis stages across tested classifiers.
Average accuracies ranged from [Formula: see text] (DT) to [Formula: see text] (MLP).
A severity index was calculated based on weighted superpixel scores, demonstrating robustness and accuracy.

Conclusions:

The developed method accurately detects disease lesions and estimates chlorosis severity.
The system shows potential for application to various plants and leaf diseases, adaptable for on-field AI edge devices.
This research contributes to advancing automated disease diagnosis in precision agriculture.