Multispectral UAV-Based Monitoring of Leek Dry-Biomass and Nitrogen Uptake across Multiple Sites and Growing Seasons

Jérémie Haumont, Peter Lootens, Simon Cool, Jonathan Van Beek, Dries Raymaekers, Eva Ampe, Tim De Cuypere, Onno Bes, Jonas Bodyn, Wouter Saeys

Research output: Contribution to journalA1: Web of Science-articlepeer-review


Leek farmers tend to apply too much nitrogen fertilizer as its cost is relatively low compared to the gross value of leek. Recently, several studies have shown that proximal sensing technologies could accurately monitor the crop nitrogen content and biomass. However, their implementation is impeded by practical limitations and the limited area they can cover. UAV-based monitoring might alleviate these issues. Studies on UAV-based vegetable crop monitoring are still limited. Because of the economic importance and environmental impact of leeks in Flanders, this study aimed to investigate the ability of UAV-based multispectral imaging to accurately monitor leek nitrogen uptake and dry biomass across multiple fields and seasons. Different modelling approaches were tested using twelve spectral VIs and the interquartile range of each of these VIs within the experimental plots as predictors. In a leave-one-flight out cross-validation (LOF-CV), leek dry biomass (DBM) was most accurately predicted using a lasso regression model (RMSEct = 6.60 g plant−1, R2= 0.90). Leek N-uptake was predicted most accurately by a simple linear regression model based on the red wide dynamic range (RWDRVI) (RMSEct = 0.22 gN plant−1, R2 = 0.85). The results showed that randomized Kfold-CV is an undesirable approach. It resulted in more consistent and lower RMSE values during model training and selection, but worse performance on new data. This would be due to information leakage of flight-specific conditions in the validation data split. However, the model predictions were less accurate for data acquired in a different growing season (DBM: RMSEP = 8.50 g plant−1, R2 = 0.77; N-uptake: RMSEP = 0.27 gN plant−1, R2 = 0.68). Recalibration might solve this issue, but additional research is required to cope with this effect during image acquisition and processing. Further improvement of the model robustness could be obtained through the inclusion of phenological parameters such as crop height.
Original languageEnglish
JournalRemote Sensing
Issue number24
Publication statusPublished - 8-Dec-2022


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