Zobrazeno 1 - 10
of 662
pro vyhledávání: '"Zongpeng Li"'
Autor:
Yafeng Li, Changchun Li, Qian Cheng, Li Chen, Zongpeng Li, Weiguang Zhai, Bohan Mao, Zhen Chen
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural producti
Externí odkaz:
https://doaj.org/article/8e87a074aca143b4b3fb1c67d2c66074
Autor:
Yafeng Li, Changchun Li, Qian Cheng, Fuyi Duan, Weiguang Zhai, Zongpeng Li, Bohan Mao, Fan Ding, Xiaohui Kuang, Zhen Chen
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3176 (2024)
Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has be
Externí odkaz:
https://doaj.org/article/6e76d356a47a4abd9b4a3b8557aa073f
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2098 (2024)
Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative
Externí odkaz:
https://doaj.org/article/881c2c35782f47009f41e7651a2193b4
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agric
Externí odkaz:
https://doaj.org/article/11240bb388df49889cf2a1d902844950
Publikováno v:
Drones, Vol 7, Iss 8, p 505 (2023)
Timely and accurate monitoring of winter wheat yields is beneficial for the macro-guidance of agricultural production and for making precise management decisions throughout the winter wheat reproductive period. The accuracy of crop yield prediction c
Externí odkaz:
https://doaj.org/article/bfeb14dbe70a4767a6ae6f32a33d32ef
Publikováno v:
Water, Vol 15, Iss 15, p 2701 (2023)
Waterlogging and salinization are considered to be the main threats to agricultural productivity and land resources in coastal areas of China. Thus far, drainage and field soil improvement programs have been ineffective. In this article, we investiga
Externí odkaz:
https://doaj.org/article/4764ebce95f44e2b82a7cae22379106a
Autor:
Weiguang Zhai, Changchun Li, Qian Cheng, Bohan Mao, Zongpeng Li, Yafeng Li, Fan Ding, Siqing Qin, Shuaipeng Fei, Zhen Chen
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3653 (2023)
Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with
Externí odkaz:
https://doaj.org/article/f1d49ecaea3c4ff782f6649d50f62e55
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2152 (2023)
Timely and accurate monitoring of the nitrogen levels in winter wheat can reveal its nutritional status and facilitate informed field management decisions. Machine learning methods can improve total nitrogen content (TNC) prediction accuracy by fusin
Externí odkaz:
https://doaj.org/article/20d617862dbc4b1bb297d0de2282fee9
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2020, Iss 1, Pp 1-24 (2020)
Abstract A cloudlet is a small-scale cloud datacenter deployed at the network edge to support mobile applications in proximity with low latency. While an individual cloudlet operates on moderate power, cloudlet clusters are well-suited candidates for
Externí odkaz:
https://doaj.org/article/ae441adcaaaf4d7291c8717e0ee99517
Autor:
Shuaipeng Fei, Muhammad Adeel Hassan, Yuntao Ma, Meiyan Shu, Qian Cheng, Zongpeng Li, Zhen Chen, Yonggui Xiao
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern preci
Externí odkaz:
https://doaj.org/article/f48ffe248f634273b200802391ad079e