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

Shyam Prasad Adhikari

doi:10.3389/fpls.2019.01404

Return

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

2019-10-31

Shyam Prasad Adhikari

Heechan Yang

Hyongsuk Kim *

DOI：10.3389/fpls.2019.01404

Original

Save

View PDF

Abstract

En 中文

Weeds in agricultural farms are aggressive growers which compete for nutrition and other resources with the crop and reduce production. The increasing use of chemicals to control them has inadvertent consequences to the human health and the environment. In this work, a novel neural network training method combining semantic graphics for data annotation and an advanced encoder-decoder network for (a) automatic crop line detection and (b) weed (wild millet) detection in paddy fields is proposed. The detected crop lines act as a guiding line for an autonomous weeding robot for inter-row weeding, whereas the detection of weeds enables autonomous intra-row weeding. The proposed data annotation method, semantic graphics, is intuitive, and the desired targets can be annotated easily with minimal labor. Also, the proposed extended skip network is an improved deep convolutional encoder-decoder neural network for efficient learning of semantic graphics. Quantitative evaluations of the proposed method demonstrated an increment of 6.29% and 6.14% in mean intersection over union (mIoU), over the baseline network on the task of paddy line detection and wild millet detection, respectively. The proposed method also leads to a 3.56% increment in mIoU and a significantly higher recall compared to a popular bounding box-based object detection approach on the task of wild-millet detection.

Keywords:

semantic graphics

convolutional neural network

autonomous weeding

crop line extraction

encoder-decoder network

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.