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DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation.

Talha Ilyas¹, Muhammad Umraiz¹, Abbas Khan¹

¹Division of Electronic Engineering, Intelligent Robots Research Center, Jeonbuk National University, Jeonju, South Korea.

Frontiers in Plant Science

|March 11, 2021

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This study introduces a novel dense attention module (DAM) for robots to accurately segment strawberries in complex farm environments. This improves ripe fruit identification for autonomous harvesting systems.

Area of Science:

Computer Vision
Robotics
Agricultural Technology

Background:

Autonomous harvesting of high-value crops like strawberries requires precise fruit identification.
Real-time segmentation of strawberries is challenging due to occlusion from leaves, stems, and trusses in unbridled farming environments.

Purpose of the Study:

To develop a dynamic feature selection mechanism for convolutional neural networks (CNNs) to improve strawberry segmentation.
To enhance the accuracy and efficiency of strawberry detection for autonomous harvesting robots.

Main Methods:

A novel dense attention module (DAM) was designed as a building block for CNNs, controlling information flow between encoder and decoder.
DAM facilitates hierarchical adaptive feature fusion by analyzing inter-channel and intra-channel relationships.

Keywords:

autonomous harvesting channel attention convolutional neural network encoder-decoder architecture fruit segmentation segmentation grad-cam semantic segmentation spatial attention

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A custom dataset of strawberries across four maturity levels and background was created and utilized.

Main Results:

The proposed DAM achieved a 4.1% increase in mean intersection over union (mIoU) compared to state-of-the-art semantic segmentation models.
A 2.32% mIoU improvement was observed over existing attention modules.
The method maintained a processing speed of 53 frames per second, ensuring real-time applicability.

Conclusions:

The dense attention module (DAM) effectively improves semantic segmentation accuracy for strawberries in challenging agricultural settings.
DAM can be easily integrated into existing CNN architectures, offering a versatile solution for agricultural robotics.
The developed method enhances the potential for accurate autonomous strawberry harvesting by improving fruit ripeness detection.