Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands

TL;DR

This paper presents a specialized multispectral remote sensing dataset and an end-to-end deep learning framework for accurate weed detection in Western Australian agricultural lands, enhancing precision agriculture practices.

Contribution

It introduces a tailored multispectral dataset and a comprehensive weed detection framework using deep learning, specifically optimized for Western Australian farming conditions.

Findings

ResNet achieved 92.13% weed detection accuracy

The framework effectively combines vegetation indices with multispectral data

High performance validated with multiple evaluation metrics

Abstract

The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts. This study constructs a tailored multispectral remote sensing dataset and an end-to-end framework for weed detection to advance precision agriculture practices. Unmanned aerial vehicles were used to collect raw multispectral data from two experimental areas (E2 and E8) over four years, covering 0.6046 km^{2} and ground truth annotations were created with GPS-enabled vehicles to manually label weeds and crops. The dataset is specifically designed for agricultural applications in Western Australia. We propose an end-to-end framework for weed detection that includes extensive preprocessing steps, such as denoising, radiometric calibration, image alignment, orthorectification, and stitching. The proposed method combines vegetation indices (NDVI, GNDVI, EVI, SAVI, MSAVI) with multispectral channels to form classification features, and employs several deep learning models to identify weeds based on the input features. Among these models, ResNet achieves the highest performance, with a weed detection accuracy of 0.9213, an F1-Score of 0.8735, an mIOU of 0.7888, and an mDC of 0.8865, validating the efficacy of the dataset and the proposed weed detection method.