Jiayi Zhang

Dr. Jiayi Zhang

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2021 in Computers and Electronics in Agriculture
Verified
Journal Article
Closed access
Jiayi ZhangJiayi Zhang
Jiayi Zhang
XQXiaolei Qiu
Xiaolei Qiu
YWYueting Wu
Yueting Wu
YZYan Zhu
Yan Zhu
QCQiang Cao
Qiang Cao
WCWeixing Cao
Weixing Cao

1 January 2022 in European Journal of Agronomy
Verified
Journal Article
Closed access
ZFZhaopeng Fu
Zhaopeng Fu
SYShanshan Yu
Shanshan Yu
Jiayi ZhangJiayi Zhang
Jiayi Zhang
HXHui Xi
Hui Xi
YGYang Gao
Yang Gao
XLXiaojun Liu
Xiaojun Liu

20 May 2022 in European Journal of Agronomy
Verified
Journal Article
Closed access
JJJie Jiang
Jie Jiang
PAPeter M. Atkinson
Peter M. Atkinson
Jiayi ZhangJiayi Zhang
Jiayi Zhang
RLRuhua Lu
Ruhua Lu
YZYouyan Zhou
Youyan Zhou
XLXiaojun Liu
Xiaojun Liu

5 March 2019 in Sensors
Verified
Journal Article
Open access
PDF file
Jiayi ZhangJiayi Zhang
Jiayi Zhang
XLXia Liu
Xia Liu
YLYan Liang
Yan Liang
Qiang CaoQiang Cao
Qiang Cao
Yongchao TianYongchao Tian
Yongchao Tian
XLXiaojun Liu
Xiaojun Liu
Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

10 November 2021 in Precision Agriculture
Verified
Journal Article
Closed access
PDF file
Jiayi ZhangJiayi Zhang
Jiayi Zhang
WWWeikang Wang
Weikang Wang
BKBrian Krienke
Brian Krienke
QCQiang Cao
Qiang Cao
YZYan Zhu
Yan Zhu
XLXiaojun Liu
Xiaojun Liu

21 October 2022 in Agricultural Systems
Verified
Journal Article
Closed access
ZFZhaopeng Fu
Zhaopeng Fu
KZKe Zhang
Ke Zhang
Jiayi ZhangJiayi Zhang
Jiayi Zhang
YZYu Zhang
Yu Zhang
QCQiang Cao
Qiang Cao
XLXiaojun Liu
Xiaojun Liu