Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland

Roland Britz,Viktoria Motsch,Andreas Schaumberger,Erich M Pötsch,Norbert Barta,Alexander Bauer,Andreas Klingler,Andreas Gronauer

doi:10.3390/rs14051154

Abstract

Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types—multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)—were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.

Highlights

Grasslands provide forage for ruminant livestock to produce meat, milk, wool, and hide [1] and for biogas production
The evaluation of the mean species group classification accuracy across all calibration and preprocessing variants showed that, on average, multilayer perceptron (MLP) provided the highest accuracy, followed by partial least squares discriminant analysis (PLS-DA) and random forest (RF), with the best representative models showing a classification accuracy of 95.7%, 88.1%, and 84.1%, respectively (Figure 3)
The vegetation composition of grasslands with respect to species group and plant part could be determined under laboratory conditions with high accuracy

Summary

Introduction

Grasslands provide forage for ruminant livestock to produce meat, milk, wool, and hide [1] and for biogas production. Permanent grasslands are the predominant type of grassland in topographically and climatically disadvantaged regions, such as mountainous areas [2]. In contrast with the intensively utilized grasslands that occur in agriculturally more favorable areas, and which usually consist of only a few plant species, the permanent grasslands in mountainous and alpine regions provide species-rich vegetation and are utilized under moderate management regimes [3]. Grassland vegetation typically comprises grasses, herbs, and legumes. These species groups and plant species represent different functional traits [4] and feed values; knowledge of their relative proportions, 4.0/).

Methods

Results

Discussion

Conclusion