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Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural

Shuli Xing¹, Malrey Lee¹

¹Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, Jeonju, Chon Buk 54896, Korea.

Sensors (Basel, Switzerland)

|September 9, 2020

Summary

Researchers developed a new feature reuse method called bridge connections to improve convolutional neural networks for detecting citrus pests and diseases, especially small ones. This method enhanced detection accuracy with minimal computational cost.

Keywords:

additional computation cost bridge connection citrus pests and diseases convolutional neural network

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Citrus fruits are vital global crops, but their yield is threatened by pests and diseases.
Existing convolutional neural network models struggle with detecting small pest and disease targets in images.

Purpose of the Study:

To develop a novel method to improve the accuracy of convolutional neural networks in detecting citrus pests and diseases, particularly for small objects.
To enhance the adaptability of these models to scale variations in image data.

Main Methods:

A new feature reuse technique, termed bridge connection, was introduced and integrated into baseline convolutional neural networks.
The performance of networks with bridge connections was evaluated against standard models like VGG-19.

Main Results:

The proposed BridgeNet-19 achieved the highest classification accuracy at 95.47%.
VGG-19 with bridge connections showed improved accuracy (94.73%) compared to pre-trained VGG-19 (95.01%).
Bridge connections reduced the need for precise sensor-to-object distance adjustments during image acquisition.

Conclusions:

Bridge connections effectively improve the accuracy of convolutional neural networks for citrus pest and disease detection without significant computational overhead.
This method enhances the robustness of detection systems, making them more flexible for practical field applications.