Yield Prediction Using Data from Unmanned Aerial Vehicles
A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".
Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 10544
Special Issue Editors
Interests: hyperspectral imaging; remote sensing; machine learning; pattern recognition; programming C; programming C++; shell programming; java programming; data analysis; programming in MATLAB; signal processing; image processing; databases
Interests: hyperspectral remote sensing; field spectroscopy; mobile and snapshot imaging spectroscopy; precision farming; agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The use of unmanned aerial vehicles (UAVs) in agriculture is only a few years old, yet the potential of this remote sensing technology for the implementation of precision agriculture can already be seen. This technology sets new standards for the spatiotemporal monitoring of fields and is also highly variable in terms of carrier systems and camera sensor technology in order to adapt to the broad range of applications.
With regard to yield, phenotypic dissection by repeated UAV campaigns could provide information on how final yield is formed during the growth phase and how direct and indirect morphological, physiological, and environmental elements influence yield. Moreover, pre-harvest yield estimates could be used to determine input factors such as nutrients, pesticides, and water in order to optimize yield potential. The need for robust multi-temporal modeling approaches across years as well as the challenges posed by highly variable imaging conditions impose limits on yield estimation using UAV data.
This Special Issue aims to present state-of-the-art methods and results of yield estimation using UAVs as platforms to collect remote sensing data in agriculture. The type of sensors used may include, but is not limited to, high resolution RGB cameras, multispectral and hyperspectral cameras, LiDAR sensors, and TIR sensors. A fusion of different UAV sensors in combination with other ground-based or satellite-based sensor systems used for modeling the yield estimation is conceivable and desirable. Different modeling approaches and comparisons between, for example, multivariate regression, decision trees, support vector machines, or artificial neural networks are also encouraged. There is no preference for the agricultural crop, but UAV data from multi-year field trials and time series datasets within the vegetation period are preferred. Additionally, contributions by validation experiments for UAV data in crop production are highly encouraged.
In this Special Issue, original research articles focusing on methodological and/or best practice and reviews are welcome. We look forward to receiving your contributions.
Prof. Dr. Uwe Knauer
Dr. András Jung
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- UAV application
- Yield estimation/ prediction
- Deep learning (DL)
- Multi-temporal
- Sensor-fusion
- Image processing
- Hyperspectral
- Multispectral
- Crop production
- Remote Sensing
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