Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection

• Published in: PloS one Vol. 18; no. 10; p. e0286732
• Main Authors: Linfeng, Wang, Yong, Liu, Jiayao, Liu, Yunsheng, Wang, Shipu, Xu
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 05.10.2023; Public Library of Science (PLoS)
• Subjects: Ablation; Accuracy; Agricultural industry; Agricultural pests; Agricultural production; Agricultural products; Agricultural research; Agriculture; Algorithms; Artificial neural networks; Biology and Life Sciences; Classification; Complexity; Computer and Information Sciences; Crop diseases; Crops; Datasets; Deep learning; Economic aspects; Geometric accuracy; Image retrieval; Insects; Machine learning; Machine vision; Management; Methods; Modelling; Neural networks; Optimization algorithms; Performance evaluation; Performance indices; Pests; Physical Sciences; Research and Analysis Methods; Rice; Technology; Vegetation; China

It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural pro...