Article
Agronomy
Zhonglin Wang, Junxu Chen, Jiawei Zhang, Xianming Tan, Muhammad Ali Raza, Jun Ma, Yan Zhu, Feng Yang, Wenyu Yang
Summary: This study assessed canopy nitrogen and carbon content of maize using hyperspectral remote sensing data and uninformative variable elimination (UVE). The results showed that the normalized difference vegetation index (NDVI) based on red edge and NIR wavebands had the highest correlation coefficients for estimating nitrogen and carbon content. UVE-PLS regression models with retained spectral parameters improved the prediction accuracy compared to PLS regression models.
Article
Environmental Sciences
Hongye Yang, Bo Ming, Chenwei Nie, Beibei Xue, Jiangfeng Xin, Xingli Lu, Jun Xue, Peng Hou, Ruizhi Xie, Keru Wang, Shaokun Li
Summary: Accurate estimation of canopy chlorophyll content (CCC) is crucial for quantitative remote sensing. This study focused on maize and found non-uniform vertical distribution of leaf chlorophyll content (LCC), which affects CCC monitoring accuracy. The study analyzed LCC profiles, leaf reflectance spectra, and their correlations, finding that leaf reflectance can be used to estimate CCC directly.
Article
Remote Sensing
Juanjuan Zhang, Weiwei Wang, Hongbo Qiao, Chaoyue Xu, Jianbiao Guo, Haiping Si, Jian Wang, Shuping Xiong, Xinming Ma
Summary: Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. In this study, the authors conducted field experiments over four years and used CR and DWT methods to process the spectra, combined with PLSR and KNN algorithms to establish the optimal LNC estimation model. The results showed that the model combining CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Article
Green & Sustainable Science & Technology
Luis Guilherme Teixeira Crusiol, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma, Chenxi Song
Summary: This research aims to evaluate the relationship between maize leaf water content (LWC) and ground-based and UAV-based hyperspectral data. The study finds that ground-based hyperspectral data outperforms UAV-based data for LWC monitoring, and HVIs and PLSR models are more suitable for LWC monitoring. The complementary use of ground-based and UAV-based hyperspectral data has the potential for maize LWC monitoring.
Article
Environmental Sciences
Caixia Yin, Xin Lv, Lifu Zhang, Lulu Ma, Huihan Wang, Linshan Zhang, Ze Zhang
Summary: This study tested the feasibility of using a UAV equipped with a hyperspectral spectrometer to monitor cotton leaf nitrogen content. The results showed that collecting UAV hyperspectral images at multiple heights improved the accuracy of assessing cotton nitrogen content.
Article
Environmental Sciences
Shishi Liu, Xiaohui Bai, Gege Zhu, Yu Zhang, Lantao Li, Tao Ren, Jianwei Lu
Summary: Estimating crop leaf nitrogen concentration (LNC) using canopy bidirectional reflectance factor (BRF) is effective but challenging due to the complex change in canopy structure. This study proposed a new method using near-infrared reflectance of vegetation (NIRV) to decouple the canopy structural effect and improve the accuracy of LNC prediction. Results showed that the NIRV-derived LNC prediction was more accurate than the BRF-derived prediction, indicating the potential of applying NIRV to enhance the LNC prediction model.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Horticulture
Renan Tosin, Isabel Pocas, Helena Novo, Jorge Teixeira, Natacha Fontes, Antonio Graca, Mario Cunha
Summary: This study developed two models to estimate Psi(pd) in a commercial vineyard, utilizing spectral data and machine learning algorithms. The first model estimated Psi(pd) based on vine canopy reflectance and selected suitable vegetation indices, while the second model optimized variables for Psi(pd) estimation based on pigments' concentrations assessed through hyperspectral reflectance. The B-MARS algorithm produced the best results with a RRMSE between 13-14% in validation.
SCIENTIA HORTICULTURAE
(2021)
Article
Environmental Sciences
Bin Wu, Wenjiang Huang, Huichun Ye, Peilei Luo, Yu Ren, Weiping Kong
Summary: The study investigated the vertical distribution of leaf chlorophyll content in winter wheat canopy using multi-angular remote sensing data and found that the response of bottom-layer chlorophyll content was weak due to the obscuring effect of upper- and middle-layers. The optimal view zenith angles or combinations improved the accuracy of chlorophyll content estimation in different layers.
Article
Agriculture, Multidisciplinary
Liang Wan, Yufei Liu, Yong He, Haiyan Cen
Summary: This study explores the use of prior knowledge and active learning to assess leaf chlorophyll content from multi-scale canopy reflectance. The results show that the hybrid method performs better when prior knowledge for model parameters is available, and the active learning method can select representative samples for model update. The proposed method obtains satisfactory estimations across different datasets, contributing to the improvement of hybrid modeling for multi-scale monitoring of leaf biochemical traits.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Agronomy
Akira Hama, Yutaro Matsumoto, Nobuhiro Matsuoka
Summary: This study utilized low-cost LiDAR technology to estimate leaf water content, demonstrating that reflectance can be a reliable indicator for indirect estimation of LWC. The results offer a potential new application in the field of plant observations.
Article
Agriculture, Multidisciplinary
G. Portz, J. P. Molin, T. F. Canata, V. I. Adamchuk
Summary: This study assesses the spatial variability of sugarcane biomass and nitrogen uptake using crop canopy reflectance and ultrasonic sensors. The results show that both sensors are able to correlate with sugarcane biomass and nitrogen uptake, with the reflectance sensor providing better assessment at the early growth stage and the ultrasonic sensor resulting in more accurate predictions at the later stages. The integration of both sensing systems improves the predictions of sugarcane biomass and nitrogen uptake, serving as an alternative for guiding local interventions during the growing season.
PRECISION AGRICULTURE
(2023)
Article
Multidisciplinary Sciences
Yanli Lu, Xiaoyu Zhang, Yuezhi Cui, Yaru Chao, Guipei Song, Caie Nie, Lei Wang
Summary: Spectral technology is effective in diagnosing N stress in maize, but it is affected by varietal differences. The responses to N stress, leaf N spectral diagnostic models, and varietal differences were analyzed in this study. The diagnostic stages for Jiyu 5817 and Zhengdan 958 were identified, and considering varietal effect in the diagnostic model improved its fit and RMSE.
SCIENTIFIC REPORTS
(2023)
Article
Environmental Sciences
Li Wang, Shuisen Chen, Dan Li, Chongyang Wang, Hao Jiang, Qiong Zheng, Zhiping Peng
Summary: This study aimed to estimate different paddy rice N traits from UAV-based hyperspectral images, and evaluate the effects of growth stages. The results suggest that the correlation between nitrogen traits and other biochemical traits is affected by the growth stage. The performance of selected VIs is relatively constant within a single growth stage but more diverse for full-growth-stage models.
Article
Forestry
Yang Liu, Pei Chen, Shuhui Chen, Ziying Hang, Songheng Jin
Summary: This study investigated the effects of nitrogen (N) addition with different organic nitrogen (ON) to inorganic nitrogen (IN) ratios on the growth and stoichiometric characteristics of Cyclocarya paliurus. The results showed that N addition with different ON:IN ratios led to a decrease in growth rate and biomass accumulation, while leaf phenolic content showed an opposite trend to leaf stoichiometry. These findings indicate that N addition caused stoichiometric imbalance and altered plant growth and accumulation of secondary metabolism substances. Consideration of nutrient limitation type and variation in N deposition components is necessary to predict the future responses of plant functional traits to N deposition.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Agronomy
Lulu Ma, Xiangyu Chen, Qiang Zhang, Jiao Lin, Caixia Yin, Yiru Ma, Qiushuang Yao, Lei Feng, Ze Zhang, Xin Lv
Summary: This study explores the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and canopy nitrogen density (CND) of drip-irrigated cotton in different growth periods. Using hyperspectral information, the study establishes nitrogen estimation models for cotton using different modeling methods. The results provide a theoretical basis for hyperspectral monitoring of crop nutrients and canopy structure.
Article
Agronomy
Xiaofei Wang, Jiawei Zhang, Xiaoqin Wang, Yibo Hu, Xiaolong Ren, Zhikuan Jia, Tiening Liu, Zhenlin Wang, Tie Cai
Summary: Film mulching ridge-furrow planting (RF) is an important dry farming mode for wheat, but it often causes lodging due to lignin accumulation in the stems. This study investigated the effects of regulating the population distribution on lodging occurrence in wheat and found that adjusting the population distribution can improve lodging resistance by enhancing the mechanical properties of the stems and promoting lignin synthesis and accumulation. The light environment plays a crucial role in lignin biosynthesis and lodging resistance.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Wei Wang, Jian-Hua Zhao, Meng-Ying Li, Wei Zhang, Muhammad Maqsood Ur Rehman, Bao-Zhong Wang, Fazal Ullah, Zheng-Guo Cheng, Li Zhu, Jin-Lin Zhang, Hong-Yan Tao, Wen-Ying Wang, You-Cai Xiong
Summary: This study investigated the physiological mechanism of yield loss in intercropped inferior species and found that plastic film mulching can alleviate water competition between maize and faba bean, reducing kernel abortion in maize.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Michael Merkle, Matthias Schumacher, Roland Gerhards
Summary: This study conducted a field experiment to test different methods and species of cover crops. The results showed that early establishment of cover crops, specifically direct sowing or sowing 10 days before harvest, had a positive impact on biomass formation and weed suppression. The performance of cover crops varied depending on the species, sowing date, and weather conditions, but a diverse cover crop mixture showed more stable performance under variable weather conditions.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Dereje Ademe, Kindie Tesfaye, Belay Simane, Benjamin F. Zaitchik, Getachew Alemayehu, Enyew Adgo
Summary: This study used simulation experiments to evaluate the effects of planting time, nitrogen rate, and crop variety choice on potato productivity in Ethiopia. The results showed that shifting planting time forward and changing the nitrogen application rate had greater productivity benefits than switching varieties. In the mid-century climate period, early planting of medium and long maturity varieties with higher nitrogen rates showed potential adaptation benefits.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Wenlong Li, Xiaobo Gu, Heng Fang, Tongtong Zhao, Rui Yin, Zhikai Cheng, Chuandong Tan, Zhihui Zhou, Yadan Du
Summary: This study aims to establish critical nitrogen dilution curves (CNDC) for maize and diagnose the nitrogen status under different mulching planting patterns. The results showed no significant differences in CNDC and its estimated parameters across years and mulching planting patterns, suggesting the establishment of a universal CNDC model for maize nitrogen diagnosis.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Guillermo A. A. Dosio, Pablo Cicore, Roberto Rizzalli
Summary: This study demonstrates through field experiments that reducing sink demand during the grain filling period in maize accelerates leaf senescence, particularly at specific phenological stages. The results also suggest that protecting grains can prevent yield reduction.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Xuan Wei, Yongjie Liu, Qiming Song, Jinping Zou, Zhiqiang Wen, Jiayu Li, Dengfei Jie
Summary: This study successfully established a model for detecting the spores of Agaricus bisporus disease using hyperspectral imaging and deep learning methods, providing a new approach for early prevention and detection of the disease.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
Article
Agronomy
Ferdaous Rezgui, Adolfo Rosati, Fatima Lambarraa-Lehnhardt, Carsten Paul, Moritz Reckling
Summary: The intensification of Mediterranean farming systems has had negative impacts on the environment, but agroforestry systems can address these issues. This study developed a practical methodology to assess the sustainability of Mediterranean agroforestry systems and found that they provide agro-environmental benefits and economic profitability, although they also require increased workload.
EUROPEAN JOURNAL OF AGRONOMY
(2024)
