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| 1 | 高光谱遥感土壤湿度信息提取研究显示文摘精准农作管理中土壤水分、土壤养分等的空间信息分布 ,可通过高光谱遥感传感器获得。本文通过对土壤的光谱反射率与土壤的表面湿度进行分析 ,比较 5种方法在反演土壤表面湿度的能力 ,并对小汤山精准农业试验区的土壤表面湿度进行高光谱填图 ,建立了较为精细的土壤水分空间分布图 ,对高光谱遥感在精准农业中深入应用进行了有效探索。 | 刘伟东 Frédéric Baret 张兵 郑兰芬 童庆禧 | 2004 | 土壤学报2004，41，5： | 82 |
| 2 | Global Wheat Head Detection(GWHD)Dataset:A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods显示文摘The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://gffzzef49fa24bca249bbhxkfwo0fxuxfv6fkb.ffgz.tsg.suse.edu.cn/and aimed at developing and benchmarking methods for wheat head detection. | Etienne David Simon Madec Pouria Sadeghi-Tehran Helge Aasen Bangyou Zheng Shouyang Liu Norbert Kirchgessner Goro Ishikawa Koichi Nagasawa Minhajul A.Badhon Curtis Pozniak Benoit de Solan Andreas Hund Scott C.Chapman Frédéric Baret Ian Stavness Wei Guo | 2020 | Plant Phenomics2020，2，1： | 13 |
| 3 | 基于数字化植物表型平台(D3P)的田间小麦冠层光截获算法开发显示文摘冠层光截获能力是反映作物品种间差异的重要功能性状,高通量表型冠层光截获对提高作物改良效率具有重要意义。本研究以小麦为研究目标,利用数字化植物表型平台(D3P)模拟生成了100种冠层结构不同的小麦品种在5个生育期的三维冠层场景,记录了从原始冠层结构中提取的绿色叶面积指数(GAI)、平均倾角(AIA)和散射光截获率(FIPAR_(dif))信息作为真实值,进一步利用上述三维小麦场景开展了虚拟的激光雷达(LiDAR)模拟实验,生成了对应的三维点云数据。基于模拟的点云数据提取了其高度分位数特征(H)和绿色分数特征(GF)。最后,利用人工神经网络(ANN)算法分别构建了从不同LiDAR点云特征(H、GF和H+GF)输入到FIPAR_(dif)、GAI和AIA的反演模型。结果表明,对于GAI、AIA和FIPAR_(dif),预测精度从高到低对应的点云特征输入为GF+H> H> GF。由此可见,H特征对提高目标表型特性的估算精度起到了重要作用。输入GF+H特征,在中等测量噪音(10%)情况下,FIPAR_(dif)和GAI的估算均获得了满意精度,R^2分别为0.95和0.98,而AIA的估算精度(R^2=0.20)还有待进一步提升。本研究基于D3P模拟数据开展,算法的实际表现还有待通过田间数据进一步验证。尽管如此,本研究验证了D3P协助表型算法开发的能力,展示了高通量LiDAR数据在估算田间冠层光截获和冠层结构方面的较高潜力。 | 刘守阳 金时超 郭庆华 朱艳 Fred Baret | 2020 | 智慧农业(中英文)2020，2，1： | 6 |
| 4 | Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series显示文摘Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology. | Shichao Jin Yanjun Su Yongguang Zhang Shilin Song Qing Li Zhonghua Liu Qin Ma Yan Ge LingLi Liu Yanfeng Ding Frédéric Baret Qinghua Guo | 2021 | Plant Phenomics2021，3，1： | 5 |
| 5 | High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass显示文摘Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes.Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments.A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest.It provided a ground spatial resolution better than 0.2 mm.A Faster Regional Convolutional Neural Network(Faster-RCNN)deep-learning model was first trained to identify the stems cross section.Results showed that the identification provided precision and recall close to 95%.Further,the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7%and robustness across the two experimental sites.The estimated stem density was also compared with the ear density measured in the field with traditional methods.A very high correlation was found with almost no bias,indicating that the stem density could be a good proxy of the ear density.The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher(80%)than that of the ear density(78%).The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section.Results show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to 2.0mm.Finally,the biovolume computed as the product of the average stem diameter,the stem density,and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%.Possible limitations of the findings and future applications are finally discussed. | Xiuliang Jin Simon Madec Dan Dutartre Benoit de Solan Alexis Comar Frédéric Baret | 2019 | Plant Phenomics2019，1，1： | 5 |
| 6 | Optimization of soil-adjusted vegetation indices显示文摘 | Geneviève Rondeaux Michael Steven Frédéric Baret | 1996 | Remote Sensing of Environment1996，，2： | 3 |
| 7 | Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods显示文摘The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version. | Etienne David Mario Serouart Daniel Smith Simon Madec Kaaviya Velumani Shouyang Liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng Norbert Kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis Pozniak David Shaner LeBauer Morten Lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo | 2021 | Plant Phenomics2021，3，1： | 2 |
| 8 | Intercalibration of vegetation indices from different sensor systems显示文摘 | Michael D Steven Timothy J Malthus Frédéric Baret Hui Xu Mark J Chopping | 2003 | Remote Sensing of Environment2003，，4： | 2 |
| 9 | Plant Phenomics:Emerging Transdisciplinary Science显示文摘Humankind is facing an unprecedented challenge to produce enough food for the coming decades because of population growth and increase in the average demand per capita,changes in climate conditions,and limitations in arable land area,as well as pressure on the water and resources.Two main avenues should be concurrently taken to increase crop productivity:improving genetics to get more efficient and resilient crops and developing optimal crop management practices.The description and understanding of crop functioning will therefore be instrumental both for genetic improvement and crop management.It will help to associate functional traits with the genome which will accelerate genetic progress by having more efficient techniques to design ideotypes adapted to particular pedoclimatic and crop management conditions and create them from the available genetic diversity. | Seishi Ninomiya Frédéric Baret Zong-Ming(Max)Cheng | 2019 | Plant Phenomics2019，1，1： | 2 |
| 10 | Optimization of soil-adjusted vegetation indices显示文摘 | Geneviève Rondeaux Michael Steven Frédéric Baret | 1996 | Remote Sensing of Environment1996，，2： | 2 |
| 11 | Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products显示文摘 | A. Verger F. Baret M. Weiss | 2008 | Remote Sensing of Environment2008，，6： | 2 |
| 12 | Optimization of soil-adjusted vegetation indices显示文摘 | Rondeaux G Steven M Baret F | 1996 | Remote Sensing of Environment1996，55，2： | 1 |
| 13 | Evaluation of canopy biophysical variable re- trieval performances from the accumulation of large swath satellite data显示文摘 | Weiss M Baret F | 1999 | Remote Sensing of Environment1999，70，： | 1 |
| 14 | Coupling canopy functioning and radiative transfer models for remote sensing data assimilation显示文摘 | Weiss M Troufleau D Baret F | 2001 | Agricultural and Forest Meteorology2001，108，2： | 1 |
| 15 | PROSPECT+SAIL models:A review of use for vegetation characterization显示文摘 | JACQUEMOUD S VERHOEF W BARET F | 2009 | Remote Sensing of Environment2009，113，1： | 1 |
| 16 | Potentials and limits of vegetation indices for LAI and APAR assessment 显示文摘 | Baret Guyot F G | 1991 | Remote Sensing of Environment1991，35，： | 1 |
| 17 | SemiOempirical iniees to aecesscarotenoidsWchlorophll a ratiofrom leaf spectral refleetance显示文摘 | Penouelas J Baret V Vilella I | 1995 | Photosynthetica1995，31，： | 1 |
| 18 | PROSPECT+ SAIL models: A review of use for vegetation characterization 显示文摘 | JACQUEMOUD S VERHOEF W BARET F | 2009 | Remote Sensing of Enviornment2009，113，1： | 1 |
| 19 | Potentials and limits of vegetaion indices for LAI and APAR assessment显示文摘 | Baret F Guyot G | 1991 | Remote Sens Environ1991，35，23： | 1 |
| 20 | Potentials and limits of vegetation indices for LAI and APAR assessment显示文摘 | Baret F Guyot G | 1991 | Remote Sensing of Environment1991，35，23： | 1 |
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