Detection of Parthenium Weed ( Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence

Parthenium weed ( Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class; all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field.