يعرض 1 - 9 نتائج من 9 نتيجة بحث عن '"Malcolm J. Hawkesford"', وقت الاستعلام: 0.92s تنقيح النتائج
  1. 1
    دورية أكاديمية
    DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

    المؤلفون: Pouria Sadeghi-Tehran, Nicolas Virlet, Eva M. Ampe, Piet Reyns, Malcolm J. Hawkesford

    المصدر: Frontiers in Plant Science, Vol 10 (2019)

    مصطلحات موضوعية: wheat ear counting, crop yield, deep learning in agriculture, semantic segmentation, superpixels, phenotyping, Plant culture, SB1-1110

    وصف الملف: electronic resource

    العلاقة: https://www.frontiersin.org/article/10.3389/fpls.2019.01176/fullTest; https://doaj.org/toc/1664-462XTest

    الوصول الحر: https://doaj.org/article/744d1dbf0c154893a346b852b3d3fe5fTest

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  2. 2
    دورية أكاديمية
    A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

    المؤلفون: Pouria Sadeghi-Tehran, Nicolas Virlet, Malcolm J. Hawkesford

    المصدر: Remote Sensing, Vol 13, Iss 5, p 898 (2021)

    مصطلحات موضوعية: hyperspectral imaging, phenotyping, hyperspectral image classification (HSI), wheat canopies, segmentation, near infrared, Science

    وصف الملف: electronic resource

    العلاقة: https://www.mdpi.com/2072-4292/13/5/898Test; https://doaj.org/toc/2072-4292Test

    الوصول الحر: https://doaj.org/article/fb474a19bf994885be56aab7dfc4dc57Test

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  3. 3
    دورية أكاديمية
    High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

    المؤلفون: Fenner H. Holman, Andrew B. Riche, Adam Michalski, March Castle, Martin J. Wooster, Malcolm J. Hawkesford

    المصدر: Remote Sensing, Vol 8, Iss 12, p 1031 (2016)

    مصطلحات موضوعية: Unmanned Aerial Vehicle, Structure from Motion, photogrammetry, crop height, phenotyping, Science

    وصف الملف: electronic resource

    العلاقة: http://www.mdpi.com/2072-4292/8/12/1031Test; https://doaj.org/toc/2072-4292Test

    الوصول الحر: https://doaj.org/article/aa43622d242b4a4d80830531696a8b24Test

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  4. 4
    A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

    المؤلفون: Malcolm J. Hawkesford, Nicolas Virlet, Pouria Sadeghi-Tehran

    المصدر: Remote Sensing; Volume 13; Issue 5; Pages: 898
    Remote Sensing, Vol 13, Iss 898, p 898 (2021)

    مصطلحات موضوعية: phenotyping, 010504 meteorology & atmospheric sciences, Hyperspectral imaging, Computer science, hyperspectral imaging, Science, near infrared, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Convolutional neural network, Spectral line, Segmentation, hyperspectral image classification (HSI), wheat canopies, segmentation, Spatial analysis, Wheat canopies, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Spectral signature, Pixel, Hyperspectral image classification (HSI, Dimensionality reduction, Imaging spectroscopy, Feature (computer vision), Phenotyping, General Earth and Planetary Sciences, Infrared

    وصف الملف: application/pdf

    الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0a820f8b5fa74bbacd1f814e6f13344Test


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  5. 5
    Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

    المؤلفون: Andrew B. Riche, Malcolm J. Hawkesford

    المصدر: Frontiers in Plant Science
    Frontiers in Plant Science, Vol 11 (2020)

    مصطلحات موضوعية: 0106 biological sciences, Germplasm, Resource (biology), phenotyping, Yield (finance), Agricultural engineering, Plant Science, Review, lcsh:Plant culture, 01 natural sciences, nitrogen use efficiency, Crop, Nutrient, wheat, medicine, G x E x M, lcsh:SB1-1110, 04 agricultural and veterinary sciences, Seasonality, medicine.disease, yield, long-term experiments, 040103 agronomy & agriculture, Trait, 0401 agriculture, forestry, and fisheries, Environmental science, Cropping, 010606 plant biology & botany

    وصف الملف: application/pdf

    الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67fd08b96a29423212ac3201e0cee16aTest
    http://europepmc.org/articles/PMC7438886Test


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  6. 6
    DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

    المؤلفون: Nicolas Virlet, Piet Reyns, Malcolm J. Hawkesford, Pouria Sadeghi-Tehran, Eva Ampe

    المصدر: Frontiers in Plant Science, Vol 10 (2019)
    Frontiers in Plant Science

    مصطلحات موضوعية: 0106 biological sciences, Computer science, Superpixels, 02 engineering and technology, Plant Science, lcsh:Plant culture, Wheat ear counting, 01 natural sciences, Convolutional neural network, Edge detection, Digital image, Robustness (computer science), Deep learning in agriculture, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, lcsh:SB1-1110, Crop yield, Cluster analysis, Original Research, 2. Zero hunger, business.industry, Deep learning, Pattern recognition, Semantic segmentation, Phenotyping, RGB color model, 020201 artificial intelligence & image processing, Artificial intelligence, business, Automated phenotyping system, 010606 plant biology & botany

    وصف الملف: application/pdf; image/jpeg; application/vnd.openxmlformats-officedocument.wordprocessingml.document

    الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a39d38dbef09d0246a911742511ca97eTest


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  7. 7
    Table_1_DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks.docx

    المؤلفون: Pouria Sadeghi-Tehran, Nicolas Virlet, Eva M. Ampe, Piet Reyns, Malcolm J. Hawkesford

    مصطلحات موضوعية: Botany, Plant Biology, Plant Systematics and Taxonomy, Plant Cell and Molecular Biology, Plant Developmental and Reproductive Biology, Plant Pathology, Plant Physiology, Plant Biology not elsewhere classified, wheat ear counting, crop yield, deep learning in agriculture, semantic segmentation, superpixels, phenotyping, automated phenotyping system

    العلاقة: https://figshare.com/articles/dataset/Table_1_DeepCount_In-Field_Automatic_Quantification_of_Wheat_Spikes_Using_Simple_Linear_Iterative_Clustering_and_Deep_Convolutional_Neural_Networks_docx/9905882Test

    الإتاحة: https://doi.org/10.3389/fpls.2019.01176.s003Test
    https://figshare.com/articles/dataset/Table_1_DeepCount_In-Field_Automatic_Quantification_of_Wheat_Spikes_Using_Simple_Linear_Iterative_Clustering_and_Deep_Convolutional_Neural_Networks_docx/9905882Test

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  8. 8
    صورة
    Image_1_DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks.jpeg

    المؤلفون: Pouria Sadeghi-Tehran, Nicolas Virlet, Eva M. Ampe, Piet Reyns, Malcolm J. Hawkesford

    مصطلحات موضوعية: Botany, Plant Biology, Plant Systematics and Taxonomy, Plant Cell and Molecular Biology, Plant Developmental and Reproductive Biology, Plant Pathology, Plant Physiology, Plant Biology not elsewhere classified, wheat ear counting, crop yield, deep learning in agriculture, semantic segmentation, superpixels, phenotyping, automated phenotyping system

    العلاقة: https://figshare.com/articles/figure/Image_1_DeepCount_In-Field_Automatic_Quantification_of_Wheat_Spikes_Using_Simple_Linear_Iterative_Clustering_and_Deep_Convolutional_Neural_Networks_jpeg/9905879Test

    الإتاحة: https://doi.org/10.3389/fpls.2019.01176.s002Test
    https://figshare.com/articles/figure/Image_1_DeepCount_In-Field_Automatic_Quantification_of_Wheat_Spikes_Using_Simple_Linear_Iterative_Clustering_and_Deep_Convolutional_Neural_Networks_jpeg/9905879Test

    View record in BASE

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  9. 9
    High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

    المؤلفون: March Castle, Andrew B. Riche, Martin J. Wooster, Malcolm J. Hawkesford, Adam Michalski, Fenner Howard Holman

    المصدر: Remote Sensing, Vol 8, Iss 12, p 1031 (2016)
    Remote Sensing; Volume 8; Issue 12; Pages: 1031
    Holman, F H, Riche, A, Michalski, A, Castle, M, Wooster, M J & Hawkesford, M 2016, ' High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing ', REMOTE SENSING, vol. 8, no. 12 . https://doi.org/10.3390/rs8121031Test

    مصطلحات موضوعية: 0106 biological sciences, phenotyping, 010504 meteorology & atmospheric sciences, Mean squared error, Pixel, Crop yield, crop height, Growing season, Unmanned Aerial Vehicle, photogrammetry, 01 natural sciences, unmanned aerial vehicle (UAV), structure from motion (SfM), light detection and ranging (LiDAR), peat fire, depth of burn (DoB), carbon emissions, Indonesia, reducing emissions from deforestation and forest degradation (REDD+), Structure from Motion, Crop, Photogrammetry, General Earth and Planetary Sciences, Environmental science, Structure from motion, lcsh:Q, Scale (map), lcsh:Science, 010606 plant biology & botany, 0105 earth and related environmental sciences, Remote sensing

    وصف الملف: application/pdf

    الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::061c8417b87ec1eb137f82dc54bac397Test
    http://www.mdpi.com/2072-4292/8/12/1031Test


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