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Full-Text Articles in Engineering
Rapeseed Seedling Stand Counting And Seeding Performance Evaluation At Two Early Growth Stages Based On Unmanned Aerial Vehicle Imagery, Biquan Zhao, Jian Zhang, Chenghai Yang, Guangsheng Zhou, Youchun Ding, Yeyin Shi, Dongyan Zhang, Jing Xie, Qingxi Liao
Rapeseed Seedling Stand Counting And Seeding Performance Evaluation At Two Early Growth Stages Based On Unmanned Aerial Vehicle Imagery, Biquan Zhao, Jian Zhang, Chenghai Yang, Guangsheng Zhou, Youchun Ding, Yeyin Shi, Dongyan Zhang, Jing Xie, Qingxi Liao
Biological Systems Engineering: Papers and Publications
The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A …
Design Of Plant Protection Uav Variable Spray System Based On Neural Networks, Sheng Wen, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, Yufeng Ge
Design Of Plant Protection Uav Variable Spray System Based On Neural Networks, Sheng Wen, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, Yufeng Ge
Biological Systems Engineering: Papers and Publications
Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, …