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crop area and leaf area index simultaneous retrieval based on spatial scaling transformation

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Science China Earth Sciences 2010年第11期53卷 1709-1716页

作者： Fan WenJie Yan BinYan Xu XiRu Peking Univ Inst RS & GIS Beijing 100871 Peoples R China

Accurate estimation of crop yields is crucial for ensuring food security. However, crops are distributed so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. As a result, unmixing of mixed pixel becomes a major problem to estimate crop yield by means of remote sensing method. Aimed at mixed pixels, we developed a new method to introduce additional information contained in the spatial scaling transformation equation to the canopy reflectance model. The crop area and LAI can be retrieved simultaneously. On the basis of a precise and simple canopy reflectance model, directional second derivative method was chosen to retrieve LAI from optimal bands of hyper-spectral data; this method can reduce the impact of the canopy non-isotropic features and soil background. To evaluate the performance of the method, Yingke Oasis, Zhangye City, Gansu Province, was chosen as the validation area. This area was covered mainly by maize and wheat. A Hyperion/EO-1 image with the 30 m spatial resolution was acquired on July 15, 2008. Images of 180 m and 1080 m resolutions were generated by linearly interpolating the original Hyperion image to coarser resolutions. Then a multi-scale image serial was obtained. Using the proposed method, we calculated crop area and the average LAI of every 1080 m pixel. A SPOT-5 classification figure serves as the validation data of crop area proportion. Results show that the pattern of crop distribution accords with the classification figure. The errors are restrained mainly to -0.1-0.1, and approximate a Normal Distribution. Meanwhile, 85 LAI values obtained using LAI-2000 Plant Canopy Analyzer, equipped with GPS, were taken as the ground reference. Results show that the standard deviation of the errors is 0.340. The method proposed in the paper is reliable.

关键词： scale transformation remote sensing crop yield estimation simultaneous retrieval crop area leaf area index

Evolution in physiochemical and cloud condensation nuclei activation properties of crop residue burning particles during photochemical aging

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Journal of Environmental Sciences 2019年第3期31卷 43-53页

作者： Yan Ma Chao Chen Junfeng Wang Youling Jiang Zewen Zheng Hui Chen Jun Zheng Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Harvard-NUIST Joint Laboratory for Air Quality and Climate (JLAQC) Nanjing University of Information Science and Technology NUIST Reading Academy Nanjing University of Information Science and Technology

As a main form of biomass burning in agricultural countries, crop residue burning is a significant source of atmospheric fine particles. In this study, the aging of particles emitted from the burning of four major crop residues in China was investigated in a smog *** particle size distribution, chemical composition and cloud condensation nuclei(CCN)activity were simultaneously measured. The properties of crop residue burning particles varied substantially among different fuel types. During aging, the particle size and mass concentration increased substantially, suggesting condensational growth by formation of secondary aerosols. The particle composition was dominated by organics. Aging resulted in considerable enhancement of organics and inorganics, with enhancement ratios of 1.24–1.44 and 1.33–1.76 respectively, as well as a continuous increase in the oxidation level of organics. Elevated CCN activity was observed during aging, with the hygroscopicity parameter κ varying from 0.16 to 0.34 for fresh particles and 0.19 to 0.40 for aged *** on the volume mixing rule, the hygroscopicity parameter of organic components(κorg) was derived. κorgexhibited an increasing tendency with aging, which was generally consistent with the tendency of the O:C ratio, indicating that the oxidation level was related to the hygroscopicity and CCN activity of organic aerosols from crop residue burning. Our results indicated that photochemical aging could significantly impact the CCN activation of crop burning aerosols, not only by the production of secondary aerosols, but also by enhancing the hygroscopicity of organic components, thereby contributing to the aerosol indirect climate forcing.

关键词： crop residue burning Aging Cloud condensation nuclei activity Hygroscopicity Organic aerosol

crop wild relatives of Kazakhstani Tien Shan:Flora,vegetation,resources

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Plant Diversity 2020年第1期42卷 19-32页

作者： Gulnara T.Sitpayeva Gulmira M.Kudabayeva Liliya A.Dimeyeva Nadezhda G.Gemejiyeva Polina V.Vesselova Institute of Botany and Phytointroduction Ministry of Education and Science050040AlmatyKazakhstan

This paper presents the results of the field study on species composition,geographical distribution,phytocoenotic diversity and resources of crop wild relatives(CWR) in Kazakhstan’s ranges of the Tien Shan *** of not only cultivated genera of crops are taken into account,but also a wider range of species of high socio-economic importance,including medicinal,fodder,essential oil and other *** of CWR includes 289 species belonging to 39 families and 145 *** them,9 species listed in the Red Data book of Kazakhstan:Pistacia vera,Rheum wittrockii,Armeniaca vulgaris,Malus sieversii,Allium pskemense,Allochrusa gypsophilloides,Sorbus sibirica,Vitis vinifera and Artemisia *** highest plant diversity is recorded in intermountain plains and river valleys where meadow vegetation forms a high abundance of forage and resource *** diversity of wild fruit plants is concentrated in gallery *** of cereals are confined to dry steppe slopes in low piedmont *** populations of almond,pistachio,plum and cherry were recorded at dry slopes of low mountain *** estimation of the raw material base for 13 resource plants is *** Rumex tianschanicus,Berberis sphaerocarpa are recommended for industrial harvesting;for local pharmacy chain-Mentha longifolia,Origanum vulgare,*** ***,Ziziphora clinopodioides,Hypericum scabrum,Hypericum perforatum,and five Rosa species.

关键词： crop wild relatives Flora Vegetation Resources Mountains Kazakhstani Tien Shan

crop diversity and pest management in sustainable agriculture

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Journal of Integrative Agriculture 2019年第9期18卷 1945-1952页

作者： HE Han-ming LIU Li-na Shahzad Munir Nawaz Haider Bashir WANG Yi YANG Jing LI Cheng-yun State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan/Faculty of Plant Protection Yunnan Agricultural University

Large-scale crop monocultures facilitate the proliferation and increasing prevalence of diseases and pest *** studies highlight the impacts of plant diversification upon pathogens,and the population dynamics of insects and beneficial organism in agricultural *** studies provide evidence that habitat manipulation techniques such as intercropping,relay,and rotation can significantly improve disease and pest *** review introduces the concept of crop diversity,considers recent insights and mechanisms underlying crop diversity,and discusses its potential for improving sustainable agricultural ***,the phytobiomes resulting from increased crop diversity are increasingly recognized for their contribution to disease and pest ***,understanding the interactions between pathogens or pests with their host phytobiome may lead to novel options for the prevention of *** advances in the agricultural systems include:(i)a better understanding of the mechanisms of interactions between crop species and genotypes;(ii)ecological progress including a better understanding of the context-dependency of those interactions;and(iii)the role of microtopographic variation in agricultural systems for priming basal resistance to multiple pests and pathogens by intercropped *** also highlight recent progress in China and the potential options for habitat management and design that enhance the ecological role of biodiversity in agroecosystems.

关键词： crop diversity pattern pest and disease management priming resistance phytobiome holobiont sustainability

AAUConvNeXt:Enhancing crop Lodging Segmentation with Optimized Deep Learning Architectures

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Plant Phenomics 2024年第2期6卷 498-516页

作者： Panli Zhang Longhui Niu Mengchen Cai Hongxu Chen Xiaobo Sun College of Engineering Northeast Agricultural UniversityHarbin 150030China

Rice lodging,a phenomenon precipitated by environmental factors or crop characteristics,presents a substantial challenge in agricultural production,notably impacting yield prediction and disaster *** that the application of conventional methodologies like visual assessment,mathematical models,and satellite remote sensing technologies has been employed in the segmentation of crop lodging,these approaches are still constrained in precision,immediacy,and capacity for large-scale evaluation.

关键词： learning lodging deep with aauconvnext architectures crop enhancing optimized segmentation

crop Phenomics and High-Throughput Phenotyping:Past Decades,Current Challenges,and Future Perspectives

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Molecular Plant 2020年第2期13卷 187-214页

作者： Wanneng Yang Hui Feng Xuehai Zhang Jian Zhang John H.Doonan William David Batchelor Lizhong Xiong Jianbing Yan Key Laboratory of crop Genetic Improvement and National Center of Plant Gene Research Huazhong Agricultural UniversityWuhan 430070P.R.China National Key Laboratory of Wheat and Maize crops Science/College of Agronomy Henan Agricultural UniversityZhengzhou 450002P.R.China The National Plant Pheno mics Centre In stitute of BiologicalEnviron mental and Rural SciencesAberystwyth UniversityAberystwythUK Department of Biosystems Engineering Auburn UniversityAuburnALUSA These authors contributed equally to this article.

Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput ***,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics ***,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming *** this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past *** then discuss the latest multi-omics research combining high-throughput phenotyping with genetic ***,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype *** is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

关键词： crop phenomics high-throughput field phenotyping root system architecture yield and quality genetic studies

crop and weed discrimination using Laws’ texture masks

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International Journal of Agricultural and Biological Engineering 2020年第1期13卷 191-197页

作者： Radhika Kamath Mamatha Balachandra Srikanth Prabhu Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia

Computers have become an integral part of human *** are used in almost every field even in *** like computer vision-based pattern recognition are being used to detect diseases and pests like weeds affecting the *** Weeds are unwanted plants growing among crops competing for nutrients,water,and *** can significantly reduce the quality and yield of the crops incurring a huge loss to the *** paper investigates the use of texture features extracted from Laws’texture masks for discrimination of Carrot crops and weeds in digital ***’texture method is one of the popular methods used to extract texture features in medical image processing,though not much explored in plant-based images or agricultural *** experiment was carried out on two categories of benchmark digital image datasets of Carrot crop and Carrot weed respectively,which are publicly available.A total of 70 texture features were *** dimensionality reduction technique was used to get the optimal *** features were then used to train the Random Forest *** results and observations from the experiment showed that the classifier achieved above 94%accuracy.

关键词： precision agriculture crop weed texture analysis classifier

Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

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Science China Earth Sciences 2010年第5期53卷 721-730页

作者： WANG DongWei1,2,3, WANG JinDi1,2 & LIANG ShunLin4 1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS, Beijing 100875, China 2 School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China 3 Haihe River Water Conservancy Commission, Tianjin 300170, China 4 Department of Geography, University of Maryland, College Park, MD 20742, USA 1. State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications CAS Beijing 100875 China2. School of Geography Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal University Beijing 100875 China3. Haihe River Water Conservancy Commission Tianjin 300170 China4. Department of Geography University of Maryland College Park MD 20742 USA

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile.

关键词： inversion leaf area index crop growth model time series MODIS

TrG2P:A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

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Plant Communications 2024年第7期5卷 16-27页

作者： Jinlong Li Dongfeng Zhang Feng Yang Qiusi Zhang Shouhui Pan Xiangyu Zhao Qi Zhang Yanyun Han Jinliang Yang Kaiyi Wang Chunjiang Zhao Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Department of Agronomy and Horticulture University of Nebraska-LincolnLincolnNE 68583USA Center for Plant Science Innovation University of Nebraska-LincolnLincolnNE 68583USA These authors contributed equally to this article

Yield prediction is the primary goal of genomic selection(GS)-assisted crop *** yield is a complex quantitative trait,making predictions from genotypic data is *** learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait ***,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation *** therefore developed TrG2P,a transfer-learning-based ***2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained ***,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned ***,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction *** applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison *** work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait *** attribute its enhanced prediction accuracy to the valuable information ava

关键词： crop genotype to phenotype transfer learning yield prediction multi-trait