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Related Experiment Video

Updated: Feb 25, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

Guan Wang¹, Yu Sun¹, Jianxin Wang¹

¹School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.

Computational Intelligence and Neuroscience

|August 1, 2017

Summary

This summary is machine-generated.

Deep learning models accurately classify apple black rot disease severity. The VGG16 model achieved 90.4% accuracy, aiding disease management and yield prediction in agriculture.

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Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Accurate disease severity estimation is crucial for food security and agricultural management.
Deep learning offers a promising approach for automated, fine-grained disease classification, overcoming limitations of traditional methods.

Purpose of the Study:

To evaluate deep convolutional neural networks for diagnosing apple black rot disease severity.
To compare the performance of models trained from scratch versus those fine-tuned using transfer learning.

Main Methods:

Utilized the PlantVillage dataset of apple black rot images, annotated by botanists into four severity stages.
Trained and evaluated various deep convolutional neural networks, including shallow networks and deep models fine-tuned with transfer learning.

Main Results:

The VGG16 deep learning model, fine-tuned with transfer learning, achieved the highest performance.
An overall accuracy of 90.4% was obtained on the hold-out test set for disease severity classification.

Conclusions:

Deep learning models, particularly VGG16 with transfer learning, demonstrate high potential for accurate apple black rot severity assessment.
The proposed model can significantly contribute to disease control strategies in modern agriculture.