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Learning Semantic Graphics Using Convolutional Encoder-Decoder Network for Autonomous Weeding in Paddy.

Shyam Prasad Adhikari¹, Heechan Yang², Hyongsuk Kim^1,2

¹Division of Electronics Engineering, Intelligent Robots Research Center (IRRC), Chonbuk National University, Jeonju, South Korea.

Frontiers in Plant Science

|November 19, 2019

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This study introduces a new AI method for detecting crop lines and weeds in paddy fields, improving autonomous weeding robot efficiency and reducing chemical use for sustainable agriculture.

Area of Science:

Agricultural Technology
Computer Vision
Artificial Intelligence

Background:

Weeds reduce crop production by competing for resources.
Chemical weed control poses risks to human health and the environment.
There is a need for efficient and sustainable weed management solutions.

Purpose of the Study:

To develop a novel neural network for automatic crop line and weed detection in paddy fields.
To enable autonomous weeding robots for both inter-row and intra-row weeding.
To improve the efficiency and reduce labor in data annotation for agricultural AI.

Main Methods:

A novel neural network training method combining semantic graphics for data annotation.
An advanced encoder-decoder network, the "extended skip network", for image analysis.

Keywords:

autonomous weeding convolutional neural network crop line extraction encoder–decoder network semantic graphics

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Utilizing detected crop lines for guiding autonomous inter-row weeding robots.

Detecting weeds for enabling autonomous intra-row weeding.

Main Results:

The proposed method achieved a 6.29% and 6.14% increment in mean intersection over union (mIoU) for paddy line and wild millet detection, respectively, over baseline networks.
Demonstrated a 3.56% increment in mIoU and significantly higher recall for wild millet detection compared to bounding box methods.
Semantic graphics annotation method proved intuitive and labor-efficient.

Conclusions:

The developed AI method enhances autonomous weeding robot capabilities for paddy fields.
The novel approach offers a more efficient and sustainable alternative to chemical weed control.
The extended skip network and semantic graphics improve AI model performance and data annotation.