Abstract
In the last decade there has been an exponential growth of research activity on the identification of correlations between vegetational indices elaborated by UAV imagery and productive and vegetative parameters of the vine. However, the acquisition and analysis of spectral data require costs and skills that are often not sufficiently available. In this context, the identification of geometric indices that allow the monitoring of spatial variability with low-cost instruments, without spectral analysis know-how but based on photogrammetry techniques with high-resolution RGB cameras, becomes extremely interesting. The aim of this work was to evaluate the potential of new canopy geometry-based indices for the characterization of vegetative and productive agronomic parameters compared to traditional NDVI based on spectral response of the canopy top. Furthermore, considering grape production as a key parameter directly linked to the economic profit of farmers, this study provides a deeper analysis focused on the development of a rapid yield forecast methodology based on UAV data, evaluating both traditional linear and machine learning regressions. Among the yield assessment models, one of the best results was obtained with the canopy thickness which showed high performance with the Gaussian process regression models (R2 = 0.80), while the yield prediction average accuracy of the best ML models reached 85.95%. The final results obtained confirm the feasibility of this research as a global yield model, which provided good performance through an accurate validation step realized in different years and different vineyards.
Highlights
In the last decade there has been an exponential growth of research activity on the identification of correlations between vegetational indices elaborated by Unmanned Aerial Vehicle (UAV) imagery and productive and vegetative parameters of the vine
In the field phenotyping area, Herrero‐Huerta et al (2020)[29] presented a research focussed on capability of Machine Learning (ML) techniques to perform grain yield prediction in soybeans by combining data from multispectral and RGB cameras equipped on UAV platforms, achieving an accuracy of over 90.72% by Random Forest (RF) and 91.36% by eXtreme Gradient Boosting (XGBoost)
The research reported that the linear regression model based on the enhanced vegetation index (EVI) provided highest performance capable of predicting the yield with a RMSE = 972 kg/ha, while the RF model based on reflectance bands was capable of predicting the protein content with an RMSE of 1.07%
Summary
In the last decade there has been an exponential growth of research activity on the identification of correlations between vegetational indices elaborated by UAV imagery and productive and vegetative parameters of the vine. Given the significant impact of climate change and terroir on vine physiological response, the added value of the research is represented by the use of a huge dataset considering both the temporal factor by examining 3 very different vegetative seasons, and spatial factor on 3 experimental sites with very different characteristics Another strength and innovation of the work is the fact of validating the predictive models identified on all the plants present in the study vineyards and not just a few sample plants, thanks to a protocol for the extraction of remote sensing indices as input of the models at the single vine level. The starting point of our research is the performance evaluation of spectral (NDVI) and geometric (canopy thickness and volume) UAV indices with respect to productive (yield and total soluble solids) and vegetative (pruning weight) parameters estimation The evaluation of these correlations was deepened by applying an in-depth analysis of the potential of the ML-based models. Considering the importance of yield prediction for farmers[21,31], the overall goal of this research is the validation of a novel yield forecast method based on a UAV image acquired several weeks before harvest
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