Application of Vision Technology and Artificial Intelligence in Smart Farming
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (270 p.)Innehållstyp: - text
- computer
- online resource
- 9783039285976
- 9783039285983
- Technology, Engineering, Agriculture, Industrial processes
- Technology: general issues
- 3D reconstruction
- CNN-LSTM
- Faster R-CNN
- Internet of Things (IoT)
- Kinect
- P. orientalis
- RFCA ResNet
- SHAP
- XGBoost algorithm
- YOLOv5
- Zanthoxylum-harvesting robot
- accumulated air temperature
- agricultural technology
- artificial intelligence (AI)
- attention mechanism
- bee mite
- big data analytics
- binarization
- body pattern image
- cascaded classification
- classification
- computer vision
- convolutional neural network
- cow udder classification
- crop phenotypic
- dairy cow
- dataset
- deep learning
- deep reinforcement learning
- deformable convolution
- discrete wavelet transform
- dual attention mechanism
- dynamic migration algorithm
- feeding behavior
- grain pest classification
- image matching
- image processing
- individual identification
- instance segmentation
- inverse distance weighting
- keypoint detection
- laying hens
- machine learning
- machine vision
- mobile edge computing
- model visualization
- multi-scale feature extraction
- multimodal fusion
- point cloud
- point cloud processing
- point cloud segmentation
- precision farming
- prediction
- recurrent neural network
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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.
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eng
Freely available e-book