Detection of Giant Rat Tail Grass Invasion Using UAV Imaging and Machine Learning

Detection of Giant Rat Tail Grass Invasion Using UAV Imaging and Machine Learning

The eradication of Giant Rat Tail (GRT) grass (Sporobolus natalensis and Sporobolus pyramidalis) in southeastern and central Queensland is essential for maintaining agricultural productivity. These invasive weeds significantly deplete soil nutrients, thereby rendering the land unsuitable for crop cultivation. Moreover, their high silica, phenolic compound, and lignin content make them unpalatable to livestock, further diminishing the utility of infested pastures. This study proposes a novel methodology that integrates drone-based imaging with machine learning techniques to classify and manage GRT grass infestations. Highresolution RGB imagery of pastureland was captured using a DJI Phantom 4 Pro UAV. The collected data were subjected to square patch segmentation, resulting in over 315574 image segments categorized into GRT and non-GRT classes. For each segment, an extensive set of 37 features, encompassing texture, radiometric, runlength characteristics and color, was extracted. Subsequent data pre-processing included the removal of tiles having no data value and the partitioning of the dataset into training and testing subsets. Five tree based machine learning classifiers-Decision Tree Classifier, Random Forest Classifier, AdaBoost Classifier, XGBoost Classifier, and XGBoost Random Forest Classifier-were developed and assessed. The classification performance was optimized by retraining the XGBoost classifier using the top 21 features identified based on their importance as determined by the already trained XGBoost classifier. Among the evaluated models, the XGBoost classifier with top 21 features achieved the highest accuracy (95.5 %), along with robust performance metrics for GRT classification, including a precision of 95.3 %, recall of 94.2 %, and F1 score of 94.7 %. These findings underscore the potential of UAV-assisted remote sensing, coupled with advanced machine learning methodologies, as a scalable and costefficient solution for the precise detection of invasive weed species, thereby facilitating their effective management and enhancing agricultural sustainability.

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