Abstract
The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.
Highlights
Adequate and affordable food supplies, which can be fulfilled only by the continuous improvement of sustainable agricultural services [1], are an urgent requirement to meet the growing demands of an increasing global population
We attempted to find a variable k, which ranged from 0.29 to 0.56, for better estimation of leaf area index (LAI), and we found that they have certain correlations with the fraction of foliage cover ( f f ) (Table 4), which is in line with a previous study of Poblete et al (2015) [22]
This study explored the potential of unmanned aerial vehicle (UAV) data as an alternative method for LAI extraction for pergola-trained vineyards
Summary
Adequate and affordable food supplies, which can be fulfilled only by the continuous improvement of sustainable agricultural services [1], are an urgent requirement to meet the growing demands of an increasing global population. The leaf area index (LAI), being one of the most important variables, is strongly correlated with canopy structure [3]. It is a key factor in many physiological and functional plant models of crop growth [4], including in vineyards [5,6]
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