Application of Vision Technology and Artificial Intelligence in Smart Farming
Artificial intelligence (AI) has been gaining traction in smart agriculture. Machine learning (ML) can be used for environmental and production performance data analysis and prediction, and computer vision (CV) can monitor abnormal phenotypes in plants and animals. They have massive potential to enh...
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| Format: | Online |
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| Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | ONIX_20240514_9783039285976_91 |
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| collection | Directory of Open Access Books |
| description | Artificial intelligence (AI) has been gaining traction in smart agriculture. Machine learning (ML) can be used for environmental and production performance data analysis and prediction, and computer vision (CV) can monitor abnormal phenotypes in plants and animals. They have massive potential to enhance the overall functioning of smart farming and reduce manual labor. This Special Issue focuses on the novel application of ML and CV in smart farming. The content of this Special Issue encompasses the use of various AI models for the in-depth analysis of quantitative data, RGB images, remote sensing images, and 3D point cloud data, thereby completing tasks such as environmental and growth state prediction, target recognition, and early disease diagnosis, improving crop growth performance and animal welfare. |
| format | Online |
| id | doab-20.500.12854ir-137489 |
| 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-1374892024-05-14T13:17:02Z Application of Vision Technology and Artificial Intelligence in Smart Farming Zou, Xiuguo Liu, Zheng Zhu, Xiaochen Zhang, Wentian Qian, Yan Li, Yuhua grain pest classification visual attention mechanism discrete wavelet transform deep learning computer vision laying hens feeding behavior Faster R-CNN model visualization P. orientalis recurrent neural network inverse distance weighting accumulated air temperature dairy cow individual identification body pattern image binarization cascaded classification Kinect crop phenotypic point cloud processing three-dimensional reconstruction singular value decomposition mobile edge computing convolutional neural network deep reinforcement learning wheat growth stages detection dynamic migration algorithm rice seed variety classification multimodal fusion machine vision point cloud YOLOv5 deformable convolution attention mechanism visual detection system Zanthoxylum-harvesting robot soil moisture prediction XGBoost algorithm SHAP dual attention mechanism multi-scale feature extraction RFCA ResNet classification 3D reconstruction the whole growth period soybean point cloud segmentation dataset bee mite image processing keypoint detection image matching cow udder classification udder features instance segmentation CNN-LSTM udder conformation precision farming smart farming agricultural technology Internet of Things (IoT) big data analytics machine learning artificial intelligence (AI) n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Artificial intelligence (AI) has been gaining traction in smart agriculture. Machine learning (ML) can be used for environmental and production performance data analysis and prediction, and computer vision (CV) can monitor abnormal phenotypes in plants and animals. They have massive potential to enhance the overall functioning of smart farming and reduce manual labor. This Special Issue focuses on the novel application of ML and CV in smart farming. The content of this Special Issue encompasses the use of various AI models for the in-depth analysis of quantitative data, RGB images, remote sensing images, and 3D point cloud data, thereby completing tasks such as environmental and growth state prediction, target recognition, and early disease diagnosis, improving crop growth performance and animal welfare. 2024-05-14T13:16:54Z 2024-05-14T13:16:54Z 2024 book ONIX_20240514_9783039285976_91 9783039285976 9783039285983 https://directory.doabooks.org/handle/20.500.12854/137489 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8646 https://mdpi.com/books/pdfview/book/8646 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-598-3 10.3390/books978-3-03928-598-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039285976 9783039285983 270 open access |
| spellingShingle | grain pest classification visual attention mechanism discrete wavelet transform deep learning computer vision laying hens feeding behavior Faster R-CNN model visualization P. orientalis recurrent neural network inverse distance weighting accumulated air temperature dairy cow individual identification body pattern image binarization cascaded classification Kinect crop phenotypic point cloud processing three-dimensional reconstruction singular value decomposition mobile edge computing convolutional neural network deep reinforcement learning wheat growth stages detection dynamic migration algorithm rice seed variety classification multimodal fusion machine vision point cloud YOLOv5 deformable convolution attention mechanism visual detection system Zanthoxylum-harvesting robot soil moisture prediction XGBoost algorithm SHAP dual attention mechanism multi-scale feature extraction RFCA ResNet classification 3D reconstruction the whole growth period soybean point cloud segmentation dataset bee mite image processing keypoint detection image matching cow udder classification udder features instance segmentation CNN-LSTM udder conformation precision farming smart farming agricultural technology Internet of Things (IoT) big data analytics machine learning artificial intelligence (AI) n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title | Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title_full | Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title_fullStr | Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title_full_unstemmed | Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title_short | Application of Vision Technology and Artificial Intelligence in Smart Farming |
| title_sort | application of vision technology and artificial intelligence in smart farming |
| topic | grain pest classification visual attention mechanism discrete wavelet transform deep learning computer vision laying hens feeding behavior Faster R-CNN model visualization P. orientalis recurrent neural network inverse distance weighting accumulated air temperature dairy cow individual identification body pattern image binarization cascaded classification Kinect crop phenotypic point cloud processing three-dimensional reconstruction singular value decomposition mobile edge computing convolutional neural network deep reinforcement learning wheat growth stages detection dynamic migration algorithm rice seed variety classification multimodal fusion machine vision point cloud YOLOv5 deformable convolution attention mechanism visual detection system Zanthoxylum-harvesting robot soil moisture prediction XGBoost algorithm SHAP dual attention mechanism multi-scale feature extraction RFCA ResNet classification 3D reconstruction the whole growth period soybean point cloud segmentation dataset bee mite image processing keypoint detection image matching cow udder classification udder features instance segmentation CNN-LSTM udder conformation precision farming smart farming agricultural technology Internet of Things (IoT) big data analytics machine learning artificial intelligence (AI) n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | grain pest classification visual attention mechanism discrete wavelet transform deep learning computer vision laying hens feeding behavior Faster R-CNN model visualization P. orientalis recurrent neural network inverse distance weighting accumulated air temperature dairy cow individual identification body pattern image binarization cascaded classification Kinect crop phenotypic point cloud processing three-dimensional reconstruction singular value decomposition mobile edge computing convolutional neural network deep reinforcement learning wheat growth stages detection dynamic migration algorithm rice seed variety classification multimodal fusion machine vision point cloud YOLOv5 deformable convolution attention mechanism visual detection system Zanthoxylum-harvesting robot soil moisture prediction XGBoost algorithm SHAP dual attention mechanism multi-scale feature extraction RFCA ResNet classification 3D reconstruction the whole growth period soybean point cloud segmentation dataset bee mite image processing keypoint detection image matching cow udder classification udder features instance segmentation CNN-LSTM udder conformation precision farming smart farming agricultural technology Internet of Things (IoT) big data analytics machine learning artificial intelligence (AI) n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20240514_9783039285976_91 |