Influence of Selected Modeling Parameters on Plant Segmentation Quality Using Decision Tree Classifiers

Florian Kitzler, Helmut Wagentristl, Reinhard W. Neugschwandtner, Andreas Gronauer and Viktoria Motsch ()
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Florian Kitzler: Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
Helmut Wagentristl: Department of Crop Sciences, Experimental Farm Groß-Enzersdorf, University of Natural Resources and Life Sciences, Schloßhofer Straße 31, Groß-Enzersdorf, 2301 Vienna, Austria
Reinhard W. Neugschwandtner: Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences, Konrad-Lorenz-Straße 24, Tulln, 3430 Vienna, Austria
Andreas Gronauer: Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
Viktoria Motsch: Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria

Agriculture, 2022, vol. 12, issue 9, 1-15

Abstract: Modern precision agriculture applications increasingly rely on stable computer vision outputs. An important computer vision task is to discriminate between soil and plant pixels, which is called plant segmentation. For this task, supervised learning techniques, such as decision tree classifiers (DTC), support vector machines (SVM), or artificial neural networks (ANN) are increasing in popularity. The selection of training data is of utmost importance in these approaches as it influences the quality of the resulting models. We investigated the influence of three modeling parameters, namely proportion of plant pixels (plant cover), criteria on what pixel to choose (pixel selection), and number/type of features (input features) on the segmentation quality using DTCs. Our findings show that plant cover and, to a minor degree, input features have a significant impact on segmentation quality. We can state that the overperformance of multi-feature input decision tree classifiers over threshold-based color index methods can be explained to a high degree by the more balanced training data. Single-feature input decision tree classifiers can compete with state-of-the-art models when the same training data are provided. This study is the first step in a systematic analysis of influence parameters of such plant segmentation models.

Keywords: plant segmentation; decision tree classifier; machine learning; computer vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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