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Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive

Jiawei Yan^1,2, Jianqing Zhao^1,2, Yucheng Cai^1,2

¹National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.

|May 13, 2023

Summary

This study refines wheat spike detection models by analyzing detection layer contributions. Removing large-scale layers and enhancing medium-scale ones improved accuracy and reduced complexity in wheat yield prediction.

Keywords:

Attention score Deep learning network Interpretive analysis Wheat spike detection

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Area of Science:

Agricultural technology
Computer vision
Deep learning

Background:

Accurate wheat spike detection is crucial for yield prediction.
Existing models often lack tailored network designs based on wheat spike characteristics.
The role of complex detection layers in wheat spike detection models is not well understood.

Purpose of the Study:

To quantitatively evaluate the contribution of different detection layers in deep learning-based wheat spike detection models.
To develop an interpretive analysis method for refining wheat spike detection networks.
To improve wheat spike detection accuracy and model efficiency.

Main Methods:

Utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to calculate attention scores in YOLOv5 detection layers.
Compared network attention areas with labeled wheat spike bounding boxes.
Refined multi-scale detection layers based on attention scores.

Main Results:

Identified that the large-scale detection layer performed poorly, while the medium-scale layer was most effective.
Removed the large-scale detection layer and added a micro-scale layer.
Enhanced feature extraction in the medium-scale layer, increasing detection accuracy and reducing model complexity.

Conclusions:

The proposed interpretive analysis method effectively evaluates detection layer contributions.
The study provides a refined wheat spike detection network with improved accuracy and efficiency.
Findings offer a valuable reference for future deep network refinement in agricultural applications.