DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

Abstract

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery. To improve the accuracy of the field-road segmentation, this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost. DR-XGBoost takes only a small amount of agricultural machine trajectory features as input. Firstly, the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator. Secondly, the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training. Thirdly, it trains XGBoost to complete the trajectory segmentation. To evaluate the effectiveness of DR-XGBoost, a series of experiments were conducted on a real trajectory dataset of agricultural machines. The model achieves a 98.2% Macro-F1 score on the dataset, which is 10.9% higher than the previous state-of-art. The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.

ConvTEBiLSTM: A Neural Network Fusing Local and Global Trajectory Features for Field-Road Mode Classification

“…Accurate classification results are essential for estimating the areas of agricultural fields precisely [10][11][12][13][14]. This estimation contributes to efficiently budgeting the input amount of agricultural production materials (e.g., seeds, fertilizers, and pesticides), which ultimately increases crop yield and improves agricultural productivity [15]. Specifically, with precise field area measurements, farmers can optimize the distribution of production materials, ensuring that each field gets the resources needed for optimal growth.…”