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

ABSTRACT: 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

SUBMITTER: Adhikari SP 

PROVIDER: S-EPMC6837080 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature