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Light Acquisition

Light Acquisition

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and

Chatla Subbarayudu¹, Mohan Kubendiran¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

|May 14, 2025

Summary

Early detection of maize leaf diseases using deep learning improves crop yield. A CNN model accurately identifies and assesses disease severity, aiding sustainable agriculture and food security.

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Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Maize (Zea mays) leaf diseases significantly reduce crop yield and market value.
Timely detection of disease intensity is crucial for effective resource management and preventing widespread crop loss.

Purpose of the Study:

To develop and evaluate a deep saliency map segmentation-based Convolutional Neural Network (CNN) for detecting, classifying, and assessing the severity of maize leaf diseases.
To improve automated disease diagnosis for enhanced crop yield and food security.

Main Methods:

Utilized a CNN model with deep saliency map segmentation for disease identification.
Employed EfficientNet-B7 for feature extraction and Hybrid Harris Hawks Optimization (HHHO) for feature selection.
Implemented Fuzzy Support Vector Machine (SVM) for final classification and severity assessment of seven maize diseases and healthy samples.

Main Results:

The proposed model achieved an average accuracy of approximately 99.47% in detecting and assessing maize leaf disease severity.
Demonstrated the effectiveness of the integrated deep learning approach in identifying various diseases like Northern Leaf Blight, Common Rust, and Gray Leaf Spot.

Conclusions:

The developed automated system offers a highly accurate solution for diagnosing maize leaf diseases and their severity.
This advancement supports sustainable agriculture by enabling timely interventions and improving overall crop yield and food security.