Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields

• Published in: PloS one Vol. 14; no. 4; p. e0215676
• Main Authors: Ma, Xu, Deng, Xiangwu, Qi, Long, Jiang, Yu, Li, Hongwei, Wang, Yuwei, Xing, Xupo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.04.2019; Public Library of Science (PLoS)
• Subjects: Agriculture; Algorithms; Aquatic plants; Artificial neural networks; Classification; Color imagery; Control; Crop Production; Engineering schools; Ground truth; Herbicides; Image acquisition; Image classification; Image processing; Image Processing, Computer-Assisted; Image segmentation; Machine learning; Manufacturing costs; Methods; Model accuracy; Neural networks; Neural Networks, Computer; Oryza - growth & development; Pattern recognition; Pixels; Plant Weeds - growth & development; Pollution; Pollution control; Remote sensing; Rice; Rice fields; Seedlings; Seedlings - growth & development; Semantic segmentation; Semantics; Test procedures; Training; Unmanned aerial vehicles; Weed control; Weeds; China

To reduce the cost of production and the pollution of the environment that is due to the overapplication of herbicide in paddy fields, the location information of rice seedlings and weeds must be detected in site-specific weed management (SSWM). With the development of deep learning, a semantic segm...