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Geostatistical Assessment of the Spatial Distribution of Some Chemical Properties in Calcareous Soils

Journal of Integrative agriculture 2012年第10期11卷 1729-1737页

作者： Asma Najafian Mahmood Dayani Hamid Reza Motaghian Habibolah Nadian Department of Soil Sciences College of Agriculture Shiraz University Shiraz *** 1ran Department of Soil Sciences Khouzestan Ramin University of Agriculture and Natural Resources Ahwaz *** Iran Department of Soil Science Faculty of Agriculture Shahrekord University Shahrekord *** Iran

Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of nutrition in Iran. The present study was conducted in a 132-ha field located in central Iran. Soil samples were collected at 0-30 cm depth and were then analyzed for total nitrogen （N）, available phosphorus （P）, available potassium （K）, available copper （Cu）, available zinc （Zn）, available iron （Fe） and available manganese （Mn）. The results showed that the contents of soil organic matter, Cu and Zn in Marvdasht＇s farms were low. The spatial distribution model and spatial dependence level for soil chemical properties varied in the field. N, K, carbonate calcium equivalent （CaCO3） and electrical conductivity （EC） data indicated the existence of moderate spatial dependence. The variograms for other variables revealed stronger spatial structure. The results showed a longer range value for available P （480 m）, followed by total N （429 m）. The value of other chemical properties values showed a shorter range （128 to 174 m）. Clear patchy distribution of N, P, K, Fe, Mn, Cu and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

关键词： geostatistics kriging spatial variability precision farming

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DEEPEYE: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization

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Big Data Mining and analytics 2018年第1期1卷 19-33页

作者： Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu College of Information Engineering Yangzhou University College of Mathematical Sciences Khartoum University State Key lab of Novel Software Tech Nanjing University College of agriculture Yangzhou University

A Non-negative Matrix Factorization(NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation,thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms.

关键词： dynamic network link prediction matrix factorization

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Wheat FHB resistance assessment using hyperspectral feature band image fusion and deep learning

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International Journal of Agricultural and Biological Engineering 2024年第2期17卷 240-249页

作者： Kun Liang Zhizhou Ren Jinpeng Song Rui Yuan Qun Zhang College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031China Jiangsu Province Engineering lab for Modern Facility agriculture Technology&Equipment Nanjing 210031China

The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant *** traditional resistance phenotype identification is still largely dependent on time-consuming manual *** this paper,the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN *** spectral data from 400-1000 nm were preprocessed by the multiple scattering correction(MSC)*** feature wavelengths(553 nm,682 nm and 714 nm)were selected by analyzing the X-loading weights(XLW)according to the absolute value of the peaks and troughs in different principal component(PC)load coefficient ***,the different fusion methods of the three feature wavelengths were explored with different weight *** R-CNN was trained on the fusion and RGB datasets with VGG16,AlexNet,ZFNet,and ResNet-50 networks ***,the other detection models SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet were used to compare with the Faster R-CNN *** a result,the Faster R-CNN with VGG16 was best with the mAP(mean Average precision)ranged from 97.7%to 98.8%.The model showed the best performance for the Fusion Image-1 ***,the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%,which was 23.89%,1.21%,0.75%,0.62%,and 8.46%higher than SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet ***,it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB *** study provided an important detection method for ensuring wheat food security.

关键词： Fusarium head blight resistance evaluation hyperspectral feature band image fusion deep learning Faster R-CNN

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Genomic insights into the genetic basis of cotton breeding in China

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Molecular Plant 2023年第4期16卷 662-677页

作者： Yiqian Li Zhanfeng Si Guoping Wang Zhuolin Shi Jinwen Chen Guoan Qi Shangkun Jin Zegang Han Wenhao Gao Yue Tian Yun Mao Lei Fang Yan Hu Hong Chen Xiefei Zhu Tianzhen Zhang zhejiang Provincial Key laboratory of Crop Genetic Resources the Advanced Seed InstitutePlant Precision Breeding AcademyCollege of Agriculture and BiotechnologyZhejiang UniversityHangzhouChina Xinjang Production and Construction Corps Seventh Division Agricultural Research Institute KuintunChina state Key laboratory of Crop Genetics and Germplasm Enhancement Nanjing Agricutural UniversityNanjingChina College of Biotechnology Jiangsu University of Science and TechnologyZhenjiangChina Key laboratory of China Northwestern Inland Region Ministry of AgricultureCotton Research InstituteXinjiang Academy of Agricultural and Reclamation ScienceShiheziChina

The excellent Upland cotton(Gossypium hirsutum)cultivars developed since 1949 have made a huge contribution to cotton production in China,the world's largest producer and consumer of ***,the genetic and genomic basis for the improvements of these cotton cultivars remains largely *** this study,we selected 16 Upland cotton cultivars with important historical status in Chinese cotton breeding and constructed a multiparent,advanced generation,intercross(MAGiC)population comprising 920 recombinant inbred lines.A genome-wide association study using the MAGIC population identified 54 genomic loci associated with lint yield and fiber *** them,25(46.30%)pleiotropic genomic loci cause simultaneous changes of lint yield and/or fiber quality traits,revealing complex trade-offs and linkage drags in Upland cotton agronomic *** sequencing data of 11 introduced ancestor cultivars and publicly available resequencing datasets of 839 cultivars developed in China during the past 70 years were integrated to explore the historical distribution and origin of the elite or selected ***,85%oftheseelitealleles were selectedandfixed fromdifferent Americanancestors,consistentwithcotton breeding practices in ***,seven elite alleles of native origin that are responsible for Fusarium wilt resistance,early maturing,good-quality fiber,and other characteristics were not found in American an-cestors but have greatly contributed to Chinese cotton breeding and wide *** together,these results provide a genetic basis for further improving cotton cultivars and reveal that the genetic composition of Chinese cotton cultivars is narrow and mainly derived from early introduced American varieties.

关键词： cotton MAGiC population GWAS whole-genome sequencing pleiotropic loci breeding history

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Genome assembly of the Brassicaceae diploid Orychophragmus violaceus reveals complex whole-genome duplication and evolution of dihydroxy fatty acid metabolism

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Plant Communications 2023年第2期4卷 93-106页

作者： Fan Huang Peng Chen Xinyu Tang Ting Zhong Taihua Yang Chinedu Charles Nwafor Chao Yang Xianhong Ge Hong An Zaiyun Li Edgar B.Cahoon Chunyu Zhang National Key lab of Crop Genetic Improvement and College of Plant Science and Technology Huazhong Agricultural UniversityWuhanChina Center for Plant Science Innovation University of Nebraska-LincolnLincolnNEUSA Department of Biochemistry University of Nebraska-LincolnLincolnNEUSA Bioinformatics and analytics Core University of Missouri-ColumbiaColumbiaMOUSA

Orychophragmus violaceus is a Brassicaceae species widely cultivated in China,particularly as a winter cover crop in northern China because of its low-temperature tolerance and low water ***,*** has also been cultivated as a potential industrial oilseed crop because of its abundant 24-carbon dihydroxy fatty acids(diOH-FAs),which contribute to superior high-temperature lubricant *** this study,we performed de novo assembly of the *** ***-genome synteny analysis of the genomes of its relatives demonstrated that *** is a diploid that has undergone an extrawhole-genome duplication(WGD)after the Brassicaceae-specific a-WGD event,with a basic chromosome number of x=*** of diOH-FAs is hypothesized to have occurred after the WGD *** on the genome and the transcriptome data from multiple stages of seed development,we predicted that OvDGAT1-1 and OvDGAT1-2 are candidate genes for the regulation of diOH-FA storage in *** *** results may greatly facilitate the development of heat-tolerant and eco-friendly plant-based lubricants using *** seed oil and improve our understanding of the genomic evolution of Brassicaceae.

关键词： Orychophragmus violaceus genome evolution dihydroxy fatty acids polyestolides lubricant oil oilseed Brassicaceae

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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

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Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data analytics lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Extracting body surface dimensions from top-view images of pigs

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International Journal of Agricultural and Biological Engineering 2018年第5期11卷 182-191页

作者： Mingzhou Lu Tomas Norton Ali Youssef Nemanja Radojkovic Alberto Peña Fernández Daniel Berckmans College of Engineering/Jiangsu Province Engineering lab for Modern Facility agriculture Technology&Equipment Nanjing Agricultural UniversityNanjing 210031China M3-BIORES-Measure Model&Manage BioresponsesKU LeuvenKasteelpark Arenberg 30B-3001 LeuvenBelgium

Continuous live weight and carcass traits estimation are important for the pig production and breeding *** is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass ***,because a pig is not constrained when an image is captured,the body does not always have a straight *** creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of *** primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig ***,the backbone line of a pig was ***,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line ***,the head and neck of the pig were removed from the pig’s contour by an ***,four length and one area parameters were *** proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of *** the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),*** parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.

关键词： body surface dimension image analysis skeleton triangulated network ellipse fitting

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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Satellite-enabled enviromics to enhance crop improvement

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Molecular Plant 2024年第6期17卷 848-866页

作者： Rafael T.Resende Lee Hickey Cibele H.Amaral Lucas L.Peixoto Gustavo E.Marcatti Yunbi Xu Universidade Federal de Goia´s(UFG) Agronomy DepartmentPlant Breeding SectorGoiania(GO)74690-900Brazil TheCROP a Precision-Breeding Startup:EnviromicsPhenomicsand GenomicsNo Zip-codeOperating VirtuallyGoiania(GO)and Sete Lagoas(MG)Brazil Queensland Alliance for agriculture and Food Innovation The University of QueenslandBrisbaneQLDAustralia Earth lab Cooperative Institute for Research in Environmental SciencesUniversity of Colorado BoulderBoulderCO 80303USA Environmental Data Science Innovation&Inclusion lab Cooperative Institute for Research in Environmental SciencesUniversity of Colorado BoulderBoulderCO 80303USA Universidade Federal de Sa˜o Joa˜o del-Rei Forest Engineering DepartmentCampus Sete LagoasSete Lagoas(MG)35701-970Brazil Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijing 100081China Peking University Institute of Advanced Agricultural Sciences WeifangShandong 261325China BGI Bioverse Shenzhen 518083China

Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental *** providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing ***-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and *** support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in ***,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and *** wealth of information,coupled with freely available climate data,fuels innovative enviromics *** enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research *** these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

关键词： envirotyping precision breeding genotype-environment interactions remote sensing predictive models enviromic information

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Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

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Information Processing in agriculture 2019年第4期6卷 502-514页

作者： Rakesh Ranjan Abhilash KChandel Lav R.Khot Haitham YBahlol Jianfeng Zhou Rick ABoydston Phillip NMiklas Department of Biological Systems Engineering Washington State UniversityPullmanWAUSA Center for precision and Automated Agricultural Systems IARECWashington State UniversityProsserWAUSA USDA-ARS Grain Legume Genetics and Physiology Research Unit ProsserWA USA

The pinto bean is one of widely consumed legume crop that constitutes over 42%of the U.S dry bean ***,limited studies have been conducted in past to assess its quantitative and qualitative yield *** remote sensing technologies can help in such ***,this study evaluates the role of ground-based multispectral imagery derived vegetation indices(VIs)for irrigated the pinto bean stress and yield *** were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52%and 100%of required *** data was acquired using a five-band multispectral imager at early,mid and late growth *** used 25 broadband VIs were derived to capture crop stress traits and yield *** component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation(rs)with abiotic stress at each growth *** difference vegetation index,nonlinear vegetation index(NLI),modified NLI and infrared percentage vegetation index(IPVI)were consistent in accounting the stress response and crop yield at all growth stages(rs>0.60,coefficient of determination(R2):0.50–0.56,P<0.05).Ten other VIs significantly accounted for crop stress at early and late ***,identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments.

关键词： Pinto bean Multispectral imaging Vegetation indices Stress assessment Yield potential

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