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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian

Malliga Subramanian¹, Narasimha Prasad L V², Janakiramaiah B³

¹Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, India.

|December 1, 2021

Summary

This summary is machine-generated.

Early detection of corn leaf diseases is crucial for crop yield. This study uses VGG16 convolutional neural networks (CNNs) and transfer learning to accurately identify corn plant diseases, achieving over 97% accuracy with reduced training time.

Keywords:

Bayesian optimization VGG16 accuracy convolutional neural networks corn transfer learning

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Corn is a vital global crop, susceptible to diseases causing significant economic losses and threatening food security.
Delayed disease diagnosis, often due to infrastructure limitations, hinders effective crop management.
Accurate and timely identification of corn leaf diseases is essential to mitigate yield reduction.

Purpose of the Study:

To develop and evaluate a VGG16-based convolutional neural network (CNN) model for classifying corn leaf diseases.
To investigate the effectiveness of transfer learning and Bayesian optimization for improving disease detection accuracy and efficiency.
To compare the performance of the proposed VGG16 model against existing methods for corn leaf disease identification.

Main Methods:

Utilized a dataset of corn leaf images encompassing three disease classes and one healthy class, sourced from web resources.
Employed VGG16, a convolutional neural network (CNN) architecture, for image classification tasks.
Conducted experiments using VGG16 as a classifier, feature extractor, and fine-tuner, incorporating Bayesian optimization for hyperparameter tuning and transfer learning to optimize training.

Main Results:

The VGG16 model, enhanced with transfer learning, achieved a test accuracy exceeding 97% in classifying corn leaf diseases.
Transfer learning significantly reduced the training time required for the VGG16 models.
The proposed approach demonstrated superior performance compared to previously established methods for corn leaf disease detection.

Conclusions:

Transfer learning applied to the VGG16 architecture provides a highly accurate and efficient method for detecting corn leaf diseases.
The developed model offers a promising solution to overcome diagnostic infrastructure challenges in agriculture.
This research contributes to safeguarding corn crop yields and ensuring global food supply stability.