Abstract
Vegetation inspection and monitoring is a time-consuming task. In the era of industrial revolution 4.0 (IR 4.0), unmanned aerial vehicles (UAV), commercially known as drones, are in demand, being adopted for vegetation inspection and monitoring activities. However, most off-the-shelf drones are least favoured by vegetation maintenance departments for on-site inspection due to limited spectral bands camera restricting advanced vegetation analysis. Most of these drones are normally equipped with a normal red, green, and blue (RGB) camera. Additional spectral bands are found to produce more accurate analysis during vegetation inspection, but at the cost of advanced camera functionalities, such as multispectral camera. Vegetation indices (VI) is a technique to maximize detection sensitivity related to vegetation characteristics while minimizing other factors which are not categorised otherwise. The emergence of machine learning has slowly influenced the existing vegetation analysis technique in order to improve detection accuracy. This study focuses on exploring VI techniques in identifying vegetation objects. The selected VIs investigated are Visible Atmospheric Resistant Index (VARI), Green Leaf Index (GLI), and Vegetation Index Green (VIgreen). The chosen machine learning technique is You Only Look Once (YOLO), which is a clever convolutional neural network (CNN) offering object detection in real time. The CNN model has a symmetrical structure along the direction of the tensor flow. Several series of data collection have been conducted at identified locations to obtain aerial images. The proposed hybrid methods were tested on captured aerial images to observe vegetation detection performance. Segmentation in image analysis is a process to divide the targeted pixels for further detection testing. Based on our findings, more than 70% of the vegetation objects in the images were accurately detected, which reduces the misdetection issue faced by previous VI techniques. On the other hand, hybrid segmentation methods perform best with the combination of VARI and YOLO at 84% detection accuracy.
Highlights
Licensee MDPI, Basel, Switzerland.Vegetation inspection is a part of power energy companies’ and environmental agencies’ responsibility to maintain vegetation away from transmission and overhead power lines
The choice of selecting an RGB-based camera unmanned aerial vehicles (UAV) may be preferable due to the lower cost acquired. This is why RGB-based camera UAVs with proper aerial image analysis techniques are considered in our research to achieve a cost-effective and accurate vegetation inspection
The results found that multispectral Vegetation indices (VI) had the best detection of symptoms; the RGB VI, especially Vegetation Index Green (VIgreen), had the ability to detect the symptoms with an acceptable result of showing symptoms based on the value difference and offered a less-costly solution
Summary
Licensee MDPI, Basel, Switzerland.Vegetation inspection is a part of power energy companies’ and environmental agencies’ responsibility to maintain vegetation away from transmission and overhead power lines. Once the vegetation reaches that zone, it will impact the transmission and distribution power lines [1]. Researchers have proposed many remote sensing techniques to reduce the cost and shorten the consumption time of vegetation inspection [1]. The YOLO model does not require high computation power to execute the training process and can provide fast detection. It has a straightforward layer of detection. As for the YOLO detection system, it has three processing steps It resizes the input image into a suitable size for YOLO detection. It runs a single convolutional network with the resized image. The detection object confidence result that meets the thresholds shows up in the final image
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