Datasets in Intelligent Agriculture
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Cross Data".
Deadline for manuscript submissions: 30 December 2026 | Viewed by 4545
Special Issue Editors
Interests: smart urban agriculture; artificial intelligence; automated control; plant phenotyping; computer vision; crop plant signaling; machine (deep) learning; food processing and safety; fluorescence imaging; hyper/multispectral imaging; Vis/NIR/MIR imaging spectroscopy
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; machine learning; unmanned aerial vehicle (UAV)-based imaging platform developments; precision agriculture; high-throughput plant phenotyping
Special Issues, Collections and Topics in MDPI journals
Interests: application of advanced ideas of robotics; remote sensing; data mining and information technology in precision agriculture; multispectral/hyperspectral imaging; spectroscopy; machine learning; geographic information system (GIS); digital mapping; biochemical sensing; phenotyping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the increasing age and decreasing number of practitioners, the application of intelligent agricultural equipment and robots in key areas of agricultural production, such as processing, storage, and quality control, is inevitable. As such, there is a need to obtain a large number of smart agricultural datasets to intelligently detect and analyze agricultural product quality and help meet the Earth's demand for a continuous supply of rich, nutritious, and delicious food. This Special Issue focuses on the breakthrough application of new-generation information technologies such as big data, artificial intelligence, and robots in the key areas of agricultural product quality control, alongside cross-integration and innovative research to solve bottleneck problems such as agricultural product information perception, intelligent decision making, and quality control.
This peer-reviewed Special Issue, titled “Datasets in Intelligent Agriculture”, focuses on the use of original datasets in this field. As such, we would like to invite experts and researchers to contribute original, high-quality research articles and reviews on the topic.
Dr. Wen-Hao Su
Dr. Zhou Zhang
Dr. Ce Yang
Guest Editors
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Keywords
- intelligent plant identification
- high-throughput phenotyping
- deep learning
- image segmentation
- weed control
- pest and disease monitoring
- yield prediction
- quality evaluation
- grading equipment
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