Machine vision applications in precision agriculture have attracted a great deal of attention. They focus on monitoring, protection, and management of various plant populations. These applications have shown potential value in reforming crucial components of plant production, including fine-grained ripeness recognition of all kinds of plants and detecting and classifying weeds, seeds, and pests for crop health, quality, and quantity enhancement. In recent decades, the extensive achievements of deep learning techniques have shown significant opportunities for almost all fields. Accordingly, many deep learning models have been presented for different types of images and have achieved promising outcomes. The deep learning-based approaches can contribute to gaining insights into the plants' inherent characteristics and the surrounding environmental elements. This research topic's primary value is providing a platform for deep learning-based applications for precision agriculture. These applications can be fairly evaluated and compared with each other. Accordingly, more effective and efficient detection and classification approaches for precision agriculture can be developed or optimized.
This Research Topic aims to collect the recent development in the Internet of Things and deep learning algorithms, including convolutional neural networks, transformer, and diffusion models, for precision agriculture in field and specialty crops. And the deep learning models are supposed to unveil the inherent characteristics of images captured from various applications in precision agriculture. Therefore, we invite researchers to contribute original research and review articles focusing (but not limited to) novel deep learning algorithms, architectures, and applications of various instruments combined with the Internet of Things and Brain-Machine Interface (BCI) devices. The presented research should include machine vision, deep learning algorithms, UAVs, BCIs, and Internet of Things sensors.
We welcome (not limited to) original research and reviews related to the deep learning architectures for image analysis in precision agriculture areas:
• Deep learning models for precision agriculture;
• Deep learning, BCI, and UAV-based crop monitoring;
• Deep learning-based Plant disease recognition and classification;
• UAV and deep learning for plant species detection and classification;
• Deep learning, IoT, and UAV-based in-field post-harvest monitoring;
• Edge-computing, BCI, and IoT applications for precision agriculture;
• BCI and UAV-based monitoring for precision agriculture;
• Deep learning and the BCI-empowered UAV applications for precision agriculture
• Optimization for deep learning algorithms in Precision Agriculture
Machine vision applications in precision agriculture have attracted a great deal of attention. They focus on monitoring, protection, and management of various plant populations. These applications have shown potential value in reforming crucial components of plant production, including fine-grained ripeness recognition of all kinds of plants and detecting and classifying weeds, seeds, and pests for crop health, quality, and quantity enhancement. In recent decades, the extensive achievements of deep learning techniques have shown significant opportunities for almost all fields. Accordingly, many deep learning models have been presented for different types of images and have achieved promising outcomes. The deep learning-based approaches can contribute to gaining insights into the plants' inherent characteristics and the surrounding environmental elements. This research topic's primary value is providing a platform for deep learning-based applications for precision agriculture. These applications can be fairly evaluated and compared with each other. Accordingly, more effective and efficient detection and classification approaches for precision agriculture can be developed or optimized.
This Research Topic aims to collect the recent development in the Internet of Things and deep learning algorithms, including convolutional neural networks, transformer, and diffusion models, for precision agriculture in field and specialty crops. And the deep learning models are supposed to unveil the inherent characteristics of images captured from various applications in precision agriculture. Therefore, we invite researchers to contribute original research and review articles focusing (but not limited to) novel deep learning algorithms, architectures, and applications of various instruments combined with the Internet of Things and Brain-Machine Interface (BCI) devices. The presented research should include machine vision, deep learning algorithms, UAVs, BCIs, and Internet of Things sensors.
We welcome (not limited to) original research and reviews related to the deep learning architectures for image analysis in precision agriculture areas:
• Deep learning models for precision agriculture;
• Deep learning, BCI, and UAV-based crop monitoring;
• Deep learning-based Plant disease recognition and classification;
• UAV and deep learning for plant species detection and classification;
• Deep learning, IoT, and UAV-based in-field post-harvest monitoring;
• Edge-computing, BCI, and IoT applications for precision agriculture;
• BCI and UAV-based monitoring for precision agriculture;
• Deep learning and the BCI-empowered UAV applications for precision agriculture
• Optimization for deep learning algorithms in Precision Agriculture
