Article
Environmental Sciences
Anting Guo, Wenjiang Huang, Yingying Dong, Huichun Ye, Huiqin Ma, Bo Liu, Wenbin Wu, Yu Ren, Chao Ruan, Yun Geng
Summary: This study utilized UAV-based hyperspectral images to monitor yellow rust disease at the field scale, and found that the VI-TF-based models had the highest accuracy in each infection period, outperforming other models. Spatial resolution significantly influenced the monitoring accuracy of TF-based models, while having a negligible impact on VI-based monitoring accuracy. The optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was found to be 10 cm.
Article
Remote Sensing
Tiansheng Li, Zhen Zhu, Jing Cui, Jianhua Chen, Xiaoyan Shi, Xu Zhao, Menghao Jiang, Yutong Zhang, Weiju Wang, Haijiang Wang
Summary: Hyperspectral technology plays a significant role in monitoring crop nitrogen status, but inaccurate estimations often result from collecting data at vertical angles. This study found that using multi-angle spectral data can improve the accuracy of estimating crop nitrogen content, particularly with the top third of leaf being the most sensitive. The model based on multi-angle composite vegetation index showed the highest accuracy in estimating winter wheat nitrogen content.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Environmental Sciences
Kai Fan, Fenling Li, Xiaokai Chen, Zhenfa Li, David J. Mulla
Summary: This study estimated the nitrogen balance index (NBI) of winter wheat based on canopy hyperspectral features and machine learning methods. The results showed that spectral transformation significantly improved the correlation between sensitive bands, vegetation indices, and NBI. The NBI prediction accuracies based on the combination of sensitive bands and vegetation indices were better than those based on univariate hyperspectral features, with better accuracy for individual growth stages compared to the whole growth stage. The random forest regression method performed the best for NBI estimation.
Article
Agronomy
Linsheng Huang, Yong Liu, Wenjiang Huang, Yingying Dong, Huiqin Ma, Kang Wu, Anting Guo
Summary: The accuracy of early and mid-term remote sensing detection of wheat stripe rust can be improved by selecting appropriate modeling methods and feature selection algorithms. In this study, the random forest (RF) algorithm combined with extreme gradient boosting (XGboost) method was explored for wheat stripe rust detection based on vegetation indices extracted from canopy-level hyperspectral measurements. The results indicate that RF combined with XGBoost can effectively improve the detection accuracy of early and mid-term wheat stripe rust at the canopy scale.
Article
Remote Sensing
Yu Ren, Wenjiang Huang, Huichun Ye, Xianfeng Zhou, Huiqin Ma, Yingying Dong, Yue Shi, Yun Geng, Yanru Huang, Quanjun Jiao, Qiaoyun Xie
Summary: Yellow rust, a severe disease affecting wheat globally, has been quantitatively estimated with a new spectral index (YROI) constructed using hyperspectral data. The study demonstrated the superior accuracy of YROI in quantifying yellow rust severity, providing new insights into the spectral response mechanism of wheat yellow rust and serving as a reference for accurate and timely quantitative identification of crop diseases on a large scale in the future.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Plant Sciences
Elena Gultyaeva, Ekaterina Shaydayuk
Summary: The significance of yellow rust caused by Puccinia striiformis (Pst) has increased worldwide, including in Russia. The study aimed to explore the yellow rust resistance potential of modern common winter wheat cultivars included in the Russian Register of Breeding Achievements. The research found that some cultivars had multiple resistance genes, which can enhance genetic diversity and overall yellow rust resistance.
Article
Environmental Sciences
Naichen Xing, Wenjiang Huang, Huichun Ye, Yu Ren, Qiaoyun Xie
Summary: The study focused on the application of spectral vegetation indices for key parameters in winter wheat growth monitoring and proposed two methods for joint retrieval. The results showed varying performance of integrated indices from the first method, and TTVI2 was identified as an excellent predictor for joint retrieval.
Article
Environmental Sciences
Qi Wang, Xiaokai Chen, Huayi Meng, Huiling Miao, Shiyu Jiang, Qingrui Chang
Summary: This study utilized machine learning methods to estimate SPAD values of winter wheat, combining vegetation indices and red-edge parameters. Different growth stages were found to have a significant impact on SPAD value estimation, with machine learning methods showing better stability in model estimation.
Article
Agriculture, Multidisciplinary
Xuan Zhang, Hui Sun, Xingxing Qiao, Xiaobin Yan, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Fahad Shafiq, Wude Yang, Chao Wang
Summary: This study optimized vegetation indices for estimating the canopy chlorophyll content (CCC) of winter wheat using different spectral processing methods. The results showed that the first derivative processing improved the correlation between the indices and crop quality traits, and the three-band index improved the monitoring accuracy of CCC.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Environmental Sciences
Qiong Zheng, Huichun Ye, Wenjiang Huang, Yingying Dong, Hao Jiang, Chongyang Wang, Dan Li, Li Wang, Shuisen Chen
Summary: A model for wheat yellow rust monitoring based on Sentinel-2 multispectral images and vegetation indices is proposed, with sensitive indices and meteorological features selected using the random forest method. Three classification methods were employed, with the Support Vector Machine algorithm showing the best performance, achieving a higher accuracy compared to pure vegetation index models.
Article
Plant Sciences
Wei Wang, Yukun Cheng, Yi Ren, Zhihui Zhang, Hongwei Geng
Summary: Through the use of multispectral images, chlorophyll meters, vegetation indices, and machine learning algorithms, this study successfully constructed estimation models for canopy chlorophyll content of winter wheat under different water treatments. The results showed higher chlorophyll content under normal irrigation compared to water limitation treatment, with different models exhibiting high estimation accuracy under different conditions.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agronomy
Jie Jiang, Peter M. Atkinson, Chunsheng Chen, Qiang Cao, Yongchao Tian, Yan Zhu, Xiaojun Liu, Weixing Cao
Summary: This study calibrated satellite remote sensing-derived models for crop growth estimation and nitrogen status diagnosis based on fine-resolution unmanned aerial vehicle (UAV) images, to map wheat growth and nitrogen status at the county scale. The results demonstrated the feasibility of combining UAV and satellite sensor images for diagnosing wheat growth and nitrogen status across large areas.
FIELD CROPS RESEARCH
(2023)
Article
Biochemical Research Methods
Alexander Koc, Firuz Odilbekov, Marwan Alamrani, Tina Henriksson, Aakash Chawade
Summary: This study used high-throughput plant phenotyping methods to predict yellow rust scores in a winter wheat field trial using spectroradiometer data. Results showed that this method has potential in wheat breeding trials for scoring yellow rust.
Article
Chemistry, Analytical
Jaafar Abdulridha, An Min, Matthew N. Rouse, Shahryar Kianian, Volkan Isler, Ce Yang
Summary: Detecting plant disease severity is important for studying the impact of diseases on cereal crops and making timely decisions. This study utilized a hyperspectral camera mounted on a drone to accurately detect the severity of wheat stem rust disease. Hyperspectral imaging can effectively discriminate between different levels of disease severity and assist breeders in selecting disease-resistant varieties more efficiently.
Article
Environmental Sciences
Haikuan Feng, Huilin Tao, Zhenhai Li, Guijun Yang, Chunjiang Zhao
Summary: This study explores the use of unmanned aerial vehicles equipped with RGB and hyperspectral cameras for monitoring crop growth. It combines multiple growth indicators to estimate a comprehensive growth index (CGI) and finds that spectral indices are more strongly correlated with the CGI than single growth-monitoring indicators. The multiple linear regression (MLR) method produces the best CGI estimates. Using hyperspectral indices provides more accurate CGI estimations compared to using RGB-image indices.
Article
Geochemistry & Geophysics
Lei Lei, Zhenhong Li, Jintao Wu, Chengjian Zhang, Yaohui Zhu, Riqiang Chen, Zhen Dong, Hao Yang, Guijun Yang
Summary: This study presented two methods, machine learning-based and structure-based, to extract leaf base and inclination angles of maize plants. The machine learning-based method demonstrated higher estimation accuracy compared to the structure-based method. The results showed good agreement with the ground truth, indicating the effectiveness of both methods in estimating leaf base and inclination angles of maize plants.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Daoyong Wang, Dongyan Zhang, Guijun Yang, Bo Xu, Yaowu Luo, Xiaodong Yang
Summary: A new wheat ear counting algorithm based on computer vision, utilizing SSRNet including FCNN and RCNN, was proposed to accurately and quickly count wheat ears in field conditions. The method effectively handles small sample datasets and accurately counts wheat ears in complex backgrounds.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Remote Sensing
Zhendong Sun, Qilei Zhu, Shangqi Deng, Xu Li, Xueqian Hu, Riqiang Chen, Guowen Shao, Hao Yang, Guijun Yang
Summary: This study proposes a dynamic quadripartite pixel model (DQPM) to calculate rice residue cover (RRC) in complex paddy field scenarios. By considering soil moisture content, DQPM achieves the best robustness under various soil moisture and RRC scenarios, resulting in more accurate calculations compared to traditional static models and dynamic dimidiate pixel models.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Sciences
Shan Jin, Wenjing Li, Yiying Cao, Glyn Jones, Jing Chen, Zhenhong Li, Qian Chang, Guijun Yang, Lynn J. Frewer
Summary: Apple production in China faces various environmental, economic, and social challenges, including risks associated with synthetic inputs, yield instability, quality deterioration, market access uncertainty, and an ageing workforce. The study suggests that existing agricultural policies are ineffective in addressing the sustainability issues within the apple production system, and recommends the development of targeted strategies to promote sustainable practices. The research demonstrates the feasibility of investigating sustainability issues in a specific industry within a cultural and policy context.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Chemistry, Analytical
Xiaoyu Song, Guijun Yang, Xingang Xu, Dongyan Zhang, Chenghai Yang, Haikuan Feng
Summary: A better understanding of wheat nitrogen status is crucial for improving N fertilizer management in precision farming. This study evaluated four different sensors for estimating winter wheat nitrogen and identified the best combinations of vegetation indices sensitive to wheat N indicators for different sensors. The results showed that the optical fluorescence sensor provided more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) was found to be the best leaf-level indicator for early wheat growth stage, while Multiplex indices were the best canopy-level indicators for early growth stage and ASD VIs provided accurate estimates for wheat N indicators at the late growth stage. The Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine provided more accurate estimates of winter wheat LNC, PNC, and NNI compared to the parametric regression (PR) and multivariable linear regression (MLR) methods.
Article
Agriculture, Multidisciplinary
Yaohui Zhu, Guijun Yang, Hao Yang, Liang Guo, Bo Xu, Zhenhai Li, Shaoyu Han, Xicun Zhu, Zhenhong Li, Glyn Jones
Summary: This study proposes a method for predicting the regional first-flowering of apple trees based on a spatial phenological survey and temperature products. The method was validated in Luochuan and Linyi, two main apple-producing areas in China. The results show that the method accurately forecasts the flowering time, which is helpful for optimizing orchard management.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Fan Wang, Chunjiang Zhao, Hao Yang, Hongzhe Jiang, Long Li, Guijun Yang
Summary: This study explores the potential of hyperspectral imaging in assessing the quality and maturity of apples. By collecting hyperspectral images and using statistical analysis, a NDSI-SCARS-PLSR model was established, which accurately estimates the firmness, soluble solids content, and starch pattern index of apples.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Linchao Li, Bin Wang, Puyu Feng, De Li Liu, Qinsi He, Yajie Zhang, Yakai Wang, Siyi Li, Xiaoliang Lu, Chao Yue, Yi Li, Jianqiang He, Hao Feng, Guijun Yang, Qiang Yu
Summary: This study integrated multi-source environmental variables into random forest and support vector machine models for wheat yield prediction in China. The results showed that using remotely sensed vegetation indices improved the precision of the models, with near-infrared reflectance being slightly better than other indices. The relative importance and partial dependence analyses identified the main predictors and their relationships with wheat yield.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Liang Han, Guijun Yang, Xiaodong Yang, Xiaoyu Song, Bo Xu, Zhenhai Li, Jintao Wu, Hao Yang, Jianwei Wu
Summary: This study uses machine learning models based on remote sensing images to detect crop lodging. The study uses Synthetic Minority Oversampling Technique and Edited Nearest Neighbors to handle imbalanced datasets, and proposes the SMOTE-ENN-XGBoost model for identifying maize lodging at the plot scale. SHapley Additive exPlanations approach is employed to interpret the features that determine lodging classification and activity prediction. The results suggest that canopy structure, spectral, and textural features should be considered simultaneously for accurate detection of crop lodging in crop breeding programs.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agronomy
Yu Zhao, Zhenhai Li, Xuexu Hu, Guijun Yang, Bujun Wang, Dandan Duan, Yuanyuan Fu, Jian Liang, Chunjiang Zhao
Summary: Timely and accurate prediction of winter wheat grain protein content is important for achieving target protein levels. A geographically weighted regression model based on meteorological factors was used to predict winter wheat GPC at the county level. The model showed higher precision than the multiple linear regressions model.
EUROPEAN JOURNAL OF AGRONOMY
(2022)
Article
Environmental Sciences
Zhenhai Li, Yu Zhao, James Taylor, Rachel Gaulton, Xiuliang Jin, Xiaoyu Song, Zhenhong Li, Yang Meng, Pengfei Chen, Haikuan Feng, Chao Wang, Wei Guo, Xingang Xu, Liping Chen, Guijun Yang
Summary: Timely monitoring of above-ground biomss is important for crop growth and yield prediction. In this study, a new crop biomass algorithm was developed to estimate winter wheat biomass using phenological observations and remote sensing data. The algorithm showed good performance in different test sites and has the potential for biomass estimation at regional scales.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Agronomy
Yujuan Huang, Jingcheng Zhang, Jingwen Zhang, Lin Yuan, Xianfeng Zhou, Xingang Xu, Guijun Yang
Summary: This study proposed a forecasting model for the Alternaria Leaf Spot (ALS) disease in apple based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. By utilizing machine learning algorithms, the study achieved an overall accuracy of 88% and Kappa of 0.53. The results demonstrated that with the aid of mobile internet technology and properly cleaned data, it is possible to collect necessary data for disease forecasting in a short time and achieve regional-scale disease prediction.
Review
Immunology
Jing Hu, Qi Guo, Congcong Liu, Qian Yu, Yuan Ren, Yueni Wu, Qin Li, Yuezhen Li, Juntao Liu
Summary: This study analyzed the immune cells of preeclampsia (PE) patients using single-cell RNA sequencing and found excessive inflammatory state in monocytes, NK cells, and B cells, as well as lower activation of memory T cells in PE patients. These findings suggest an immune imbalance in PE and provide potential therapeutic strategies for monitoring and treating the condition.
INTERNATIONAL REVIEWS OF IMMUNOLOGY
(2022)
Article
Forestry
Jinghua Wang, Xiang Li, Guijun Yang, Fan Wang, Sen Men, Bo Xu, Ze Xu, Haibin Yang, Lei Yan
Summary: In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. The experimental results showed that the Improved YOLOv5 model achieved higher precision and recall rates compared to the original models. This research is of great significance for the scientific planning of tea bud picking and improving the production efficiency and quality of tea production.
Article
Agriculture, Multidisciplinary
Jing Chen, Chunjiang Zhao, Glyn Jones, Hao Yang, Zhenhong Li, Guijun Yang, Liping Chen, Yongchang Wu
Summary: Rapid socio-economic changes in China have created opportunities for the application of precision agriculture. An experiment evaluating the economic benefits of precision seeding and land leveling methods showed that they can increase crop yield and reduce soil nitrogen concentration. Considering the long-term benefits, the economic assessment needs to accurately estimate the return on investment.
ARTIFICIAL INTELLIGENCE IN AGRICULTURE
(2022)
