Yield Prediction Using Data from Unmanned Aerial Vehicles

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

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