“Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development
The aim of this Special Issue is to explore and support the evolution of emerging digital technology applications in agriculture and biology, including but not limited to agriculture, data collection, data mining, bioinformatics, genomics, and phenomics, as well as applications of machine learning a...
Gardado en:
| Formato: | Online |
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| Idioma: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Subjects: | |
| Acceso en liña: | ONIX_20240514_9783725808182_502 |
| Tags: |
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| _version_ | 1869524782711570432 |
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| collection | Directory of Open Access Books |
| description | The aim of this Special Issue is to explore and support the evolution of emerging digital technology applications in agriculture and biology, including but not limited to agriculture, data collection, data mining, bioinformatics, genomics, and phenomics, as well as applications of machine learning and artificial intelligence. The development of a community to support this goal requires the cross-linking and integration of multiple sources of agricultural research across 3S technologies (remote sensing—RS; geographic information systems—GIS; global positioning systems—GPS). |
| format | Online |
| id | doab-20.500.12854ir-137887 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1378872024-05-14T14:51:58Z “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development Zhang, Jian Goebel, Randy G. Wu, Zhihai corn seeds image identification multi-scale feature fusion deep learning machine vision improved DeepLabV3+ attention mechanism image segmentation strawberry weed identification Faster-R-CNN FPN ResNeXt improved DeepLabv3+ model semantic segmentation transformer weed recognition appearance quality identification of ginseng activation function loss function panchagavya organic fertilizer liquid fertilizer automated fertilizer production drip irrigation system automated irrigation soil texture identification DLAC-CNN-RF model accuracy laser heterodyne radiometer carbon dioxide methane nitrous oxide field measurement pest identification FCN DenseNet maize leaf disease digital agriculture seed metering device monitoring system photoelectric sensor miss multiples flow rate smart agriculture citrus diseases generative adversarial network classification network FastGAN EfficientNet septoriosis Septoria tritici blotch hyperspectral signature hyperspectral disease detection data science neural network wheat seed vigor spectral detection technology image detection technology Information communication technology agriculture ensemble learning Gaussian probabilistic method function convolutional neural network support vector machines crop phenotype maize stem diameter morphological gradient target region YOLOv7-tiny-Apple small target fruit detection and counting crop seedling detection dense target detection lightweight transformer YOLOv5 edible fungi fruit body disease recognition ShuffleNetV2 spatial data quality data quality assessment data quality dimensions interpolation classification n/a thema EDItEUR::P Mathematics and Science::PS Biology, life sciences The aim of this Special Issue is to explore and support the evolution of emerging digital technology applications in agriculture and biology, including but not limited to agriculture, data collection, data mining, bioinformatics, genomics, and phenomics, as well as applications of machine learning and artificial intelligence. The development of a community to support this goal requires the cross-linking and integration of multiple sources of agricultural research across 3S technologies (remote sensing—RS; geographic information systems—GIS; global positioning systems—GPS). 2024-05-14T14:51:54Z 2024-05-14T14:51:54Z 2024 book ONIX_20240514_9783725808182_502 9783725808182 9783725808175 https://directory.doabooks.org/handle/20.500.12854/137887 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9148 https://mdpi.com/books/pdfview/book/9148 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0817-5 10.3390/books978-3-7258-0817-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725808182 9783725808175 316 open access |
| spellingShingle | corn seeds image identification multi-scale feature fusion deep learning machine vision improved DeepLabV3+ attention mechanism image segmentation strawberry weed identification Faster-R-CNN FPN ResNeXt improved DeepLabv3+ model semantic segmentation transformer weed recognition appearance quality identification of ginseng activation function loss function panchagavya organic fertilizer liquid fertilizer automated fertilizer production drip irrigation system automated irrigation soil texture identification DLAC-CNN-RF model accuracy laser heterodyne radiometer carbon dioxide methane nitrous oxide field measurement pest identification FCN DenseNet maize leaf disease digital agriculture seed metering device monitoring system photoelectric sensor miss multiples flow rate smart agriculture citrus diseases generative adversarial network classification network FastGAN EfficientNet septoriosis Septoria tritici blotch hyperspectral signature hyperspectral disease detection data science neural network wheat seed vigor spectral detection technology image detection technology Information communication technology agriculture ensemble learning Gaussian probabilistic method function convolutional neural network support vector machines crop phenotype maize stem diameter morphological gradient target region YOLOv7-tiny-Apple small target fruit detection and counting crop seedling detection dense target detection lightweight transformer YOLOv5 edible fungi fruit body disease recognition ShuffleNetV2 spatial data quality data quality assessment data quality dimensions interpolation classification n/a thema EDItEUR::P Mathematics and Science::PS Biology, life sciences “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title | “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title_full | “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title_fullStr | “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title_full_unstemmed | “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title_short | “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development |
| title_sort | smart agriculture information technology and agriculture cross discipline research and development |
| topic | corn seeds image identification multi-scale feature fusion deep learning machine vision improved DeepLabV3+ attention mechanism image segmentation strawberry weed identification Faster-R-CNN FPN ResNeXt improved DeepLabv3+ model semantic segmentation transformer weed recognition appearance quality identification of ginseng activation function loss function panchagavya organic fertilizer liquid fertilizer automated fertilizer production drip irrigation system automated irrigation soil texture identification DLAC-CNN-RF model accuracy laser heterodyne radiometer carbon dioxide methane nitrous oxide field measurement pest identification FCN DenseNet maize leaf disease digital agriculture seed metering device monitoring system photoelectric sensor miss multiples flow rate smart agriculture citrus diseases generative adversarial network classification network FastGAN EfficientNet septoriosis Septoria tritici blotch hyperspectral signature hyperspectral disease detection data science neural network wheat seed vigor spectral detection technology image detection technology Information communication technology agriculture ensemble learning Gaussian probabilistic method function convolutional neural network support vector machines crop phenotype maize stem diameter morphological gradient target region YOLOv7-tiny-Apple small target fruit detection and counting crop seedling detection dense target detection lightweight transformer YOLOv5 edible fungi fruit body disease recognition ShuffleNetV2 spatial data quality data quality assessment data quality dimensions interpolation classification n/a thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| topic_facet | corn seeds image identification multi-scale feature fusion deep learning machine vision improved DeepLabV3+ attention mechanism image segmentation strawberry weed identification Faster-R-CNN FPN ResNeXt improved DeepLabv3+ model semantic segmentation transformer weed recognition appearance quality identification of ginseng activation function loss function panchagavya organic fertilizer liquid fertilizer automated fertilizer production drip irrigation system automated irrigation soil texture identification DLAC-CNN-RF model accuracy laser heterodyne radiometer carbon dioxide methane nitrous oxide field measurement pest identification FCN DenseNet maize leaf disease digital agriculture seed metering device monitoring system photoelectric sensor miss multiples flow rate smart agriculture citrus diseases generative adversarial network classification network FastGAN EfficientNet septoriosis Septoria tritici blotch hyperspectral signature hyperspectral disease detection data science neural network wheat seed vigor spectral detection technology image detection technology Information communication technology agriculture ensemble learning Gaussian probabilistic method function convolutional neural network support vector machines crop phenotype maize stem diameter morphological gradient target region YOLOv7-tiny-Apple small target fruit detection and counting crop seedling detection dense target detection lightweight transformer YOLOv5 edible fungi fruit body disease recognition ShuffleNetV2 spatial data quality data quality assessment data quality dimensions interpolation classification n/a thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| url | ONIX_20240514_9783725808182_502 |