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PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and

Punam Bedi¹, Pushkar Gole¹, Sudeep Marwaha²

¹Department of Computer Science, University of Delhi, New Delhi, India.

Frontiers in Plant Science

|January 23, 2024

Summary

A new framework, PDSE-Lite, uses Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) for early plant disease diagnosis and severity estimation. This approach significantly reduces the need for extensive manual data annotation, offering a more efficient solution.

Keywords:

AI in agriculture Convolutional Autoencoder automatic plant disease severity estimation deep learning few-shot learning

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate plant disease diagnosis and severity estimation are crucial for timely agricultural interventions.
Existing methods often require large, manually annotated datasets, posing a significant challenge.
Reducing reliance on extensive data annotation is key to improving efficiency in plant disease management.

Purpose of the Study:

To propose a lightweight framework, PDSE-Lite, for plant disease severity estimation using limited training data.
To develop a system that reduces human effort in data annotation for plant disease analysis.
To enable early and accurate diagnosis and severity quantification of plant diseases.

Main Methods:

A two-stage approach utilizing a Convolutional Autoencoder (CAE) for image reconstruction.
Integration of Few-Shot Learning (FSL) with pre-trained CAE layers for classification and segmentation.
Disease severity calculation based on the percentage of segmented diseased leaf pixels.

Main Results:

PDSE-Lite accurately detected healthy and four types of apple tree diseases with only two training samples per class.
The framework precisely segmented diseased areas from leaf images.
Performance evaluation demonstrated superior accuracy compared to existing state-of-the-art techniques.
Statistical hypothesis testing confirmed precise disease severity estimation with a 99% confidence interval.

Conclusions:

PDSE-Lite offers a promising solution for early-stage plant disease diagnosis and severity estimation.
The framework significantly reduces the need for large-scale manual data annotation.
This approach enhances the efficiency and accessibility of plant disease management tools.