Abstract
Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.
Highlights
IntroductionPhenotypes from controlled conditions rarely agree with those in field environments (Nelissen et al, 2014; Poorter et al, 2016)
Plant phenotyping address the description of the plant’s anatomical, physiological and biochemical properties (Walter et al, 2015)
The objective of this study was to develop a new application for the classification of a large number of grapevine (Vitis vinifera L.) varieties using on-the-go hyperspectral imaging under field conditions and machine learning algorithms
Summary
Phenotypes from controlled conditions rarely agree with those in field environments (Nelissen et al, 2014; Poorter et al, 2016). The development of new technologies and methodologies for the precise phenotyping and monitoring of grapevines under field conditions would definitely improve grape quality (and, wine quality), a key factor for the industry. Ampelography aims at extracting morphological differences between the leaves and grape berries, but it has always required specialized human resources This methodology has gradually made way to modern and more precise identification approaches, such as wet chemistry (Altube et al, 1991) or DNA analysis (Sefc et al, 2001; Borrego et al, 2002; Pelsy et al, 2010). The difficulty to fast and apply these techniques and their destructive nature makes them unable to be translated to a real time in-field application
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