Abstract
Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.
Highlights
Weed detection has conventionally been carried out through either ground-based platforms or remotely sensed images acquired from aircrafts, unmanned aerial vehicles (UAV) or satellites [1,2]through examination of remote sensing pixel data unable to provide precise crop detection due to pixel variation and spectral behavior resemblance [3]
The main aim of the current study is to provide a robust solution in utilizing remote sensing data for the purpose of plant species identification to contribute to the larger field of precision agriculture practices
It is obvious that the Multilayer Perceptron with Automatic Relevance Determination (MLP-Automatic relevance determination (ARD)) classifier has managed to classify correctly >99% of the pixels of both categories, and only few pixels (
Summary
Weed detection has conventionally been carried out through either ground-based platforms or remotely sensed images acquired from aircrafts, unmanned aerial vehicles (UAV) or satellites [1,2]. Through examination of remote sensing pixel data unable to provide precise crop detection due to pixel variation and spectral behavior resemblance [3]. The main aim of the current study is to provide a robust solution in utilizing remote sensing data for the purpose of plant species identification to contribute to the larger field of precision agriculture practices. Silybum marianum is a weed accountable for major loss of crop yield and is tough to eradicate. Herbicides are costly and pollute the rural and natural ecosystems. Due to its height and thorny leaves, it may inhibit the movement of livestock across grasslands. Khan et al (2011) [4]
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