A Comparative Study of Vetiveria zizanioides Leaf Segmentation Techniques Using Visible, Infrared, and Thermal Camera Sensors in an Outdoor Environment

Abstract

A camera vision system is a fast and effective approach to monitoring leaves. It can be used to monitor plant growth, detect diseases, and conduct plant phenotyping. However, due to the outdoor environment of plants, it becomes challenging to use. This paper addresses the problems of Vetiveria zizanioides leaf segmentation by comparing different camera types and segmentation techniques. Visible, no infrared filter (NoIR), and thermal cameras interfaced in an embedded device were used to capture plants during the day and at night. Several popular thresholding techniques and the K-Means algorithm were employed for leaf segmentation, and their performance was measured using Recall, Precision, and F1 score. The comparison results show that the visible camera achieved the best performance on daytime images, with the highest Recall of 0.934 using Triangle thresholding, the highest Precision of 0.751 using K-Means (K = 3), and the highest F1 score of 0.794 using Multi-Otsu thresholding. For nighttime images, the highest Recall of 0.990 was achieved by the thermal camera using Isodata and Otsu thresholding, the highest Precision of 0.572 was achieved by the NoIR camera using K-Means (K = 3), and the highest F1 score of 0.636 was achieved by the NoIR camera using K-Means (K = 3). To compare the leaf segmentation performance of the thresholding techniques and the K-Means algorithm between our image dataset and the well-known plant image dataset, we also evaluated the methods using the Ara2012 image dataset. The results showed that K-Means (K-3) achieved the best performance. The execution time of K-Means was about 3 s, which was longer than the thresholding techniques. However, it is still acceptable for the real-time plant monitoring system.