A5101609473- Smart Agriculture and AI
- Image Processing Techniques and Applications
- Medical Research and Treatments
- Plant Molecular Biology Research
- Animal and Plant Science Education
- Dengue and Mosquito Control Research
- Food Supply Chain Traceability
- Plant Gene Expression Analysis
- Fire Detection and Safety Systems
- Chromosomal and Genetic Variations
- Advanced Image Processing Techniques
- Pharmacological Effects of Natural Compounds
- Food Quality and Safety Studies
- Plant Parasitism and Resistance
- Remote Sensing in Agriculture
- Advanced Chemical Sensor Technologies
- Spectroscopy and Chemometric Analyses
Anhui Agricultural University
2008-2020
For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an has a significant impact on the accuracy pre-trained classifiers. To address this problem, we propose generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This effectively transform unclear into clear high-resolution images. Additionally, weight sharing scheme our...
The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because various pests. And crop production is decreased due to categories insects. Deep learning technologies significantly increased efficiency identifying controlling agricultural pests attack. However, images obtained often obscure unclear sparse density cameras deployed in real farmland. This always makes difficult recognize monitor. Additionally, existing classification segmentation...