Findings from China Agricultural University Provides New Data about Risk Management (A Transfer-learning-based Feature Classification Algorithm for Uav Imagery In Crop Risk Management)

2020 JUL 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on Risk Management are discussed in a new report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The development of precision agriculture puts forward higher requirements for the construction of an ecological irrigation area, some of which are the control of crop risk and the good circulation of the environment. In this paper, a method for extracting crops and weeds using remote sensing images of unmanned aerial vehicle (UAV) is proposed.”

Funders for this research include National Key R&D Program of China, National Natural Science Foundation of China, Jilin Province Key RD Plan Project, Open Fund of Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology, Jiangsu University, Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Chinese Universities Scientific Fund.

Our news editors obtained a quote from the research from China Agricultural University, “The main three crops (wheat, peanut, maize) and three weeds (Chenopodium album, Humulus scandens, Xanthium sibiricum Patrin ex Widder) in the ecological irrigation area are collected by UAV. By manual labeling, 2,287 training sets and 979 test sets are formed. AlexNet, a transfer neural network, is trained in a single central processing unit (CPU), single graphics processing unit (GPU), and double GPUs to test the complexity of the algorithm. The classification results show that the accuracy of the Chenopodium album is 100%, Humulus scandensis 99.07%, Xanthium sibiricum Patrin ex Widder is 100%, wheat is 99.49%, peanut is 100%, and maize is 99.05%. The overall accuracy rate is 99.69%. The method proposed in this paper can accurately extract crops and weeds and calculate the quantity. The density of each weed can also be calculated in conjunction with the proposed density calculation method.”

According to the news editors, the research concluded: “It can provide a reference for the precise application of pesticides, thereby improving crop risk management capabilities.”

For more information on this research see: A Transfer-learning-based Feature Classification Algorithm for Uav Imagery In Crop Risk Management. Desalination and Water Treatment, 2020;181():330-337. Desalination and Water Treatment can be contacted at: Desalination Publ, 36 Walcott Valley Drive, Hopkinton, MA 01748, USA.

The news editors report that additional information may be obtained by contacting Jian Chen, China Agricultural University, College of Engineering, Beijing 100083, People’s Republic of China. Additional authors for this research include Shubo Wang, Yi Cao, Hao Meng, Yu Han and Yue Pan.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.5004/dwt.2020.25200. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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