## 1 Introduction  
Image processing approaches have been used in a range of applications. One of the challenging and interesting applications is plant monitoring, where camera systems are employed to capture information of plants for further tasks, such as leaf detection and counting [1; 2; 3; 4; 5], growth monitoring [6; 7; 8; 9], leaf classification [10], disease detection [11; 12; 13], stress monitoring [14; 15], and phenotyping [16; 17; 18]. A leaf segmentation technique is commonly used as the preliminary process, which extracts the leaf area from the background in the image. Once the leaf area is detected, image analysis of the leaf object can be carried out. The most common leaf segmentation techniques are thresholding-based approaches, watershed, random walker, K-Means, artificial neural networks (ANNs), and color-index-based approaches. HSV color thresholding was used in [19] for rice plant segmentation as the initial stage of plant height measurement. Color thresholding using the CIELAB color model was used in [20] for vine leaf segmentation from real environment images. HSI color segmentation was employed in [9] to extract leafy vegetables using a Kinect sensor. In [21], thresholding using a new color model was used to measure the leaf area of lettuce plants. Otsu thresholding based on the hue component was used for leaf area measurement in [22]. A software tool for estimating the leaf area was developed in [23] using RGB color thresholding to extract the leaf from the background. The watershed algorithm has been used for rosette plant leaf segmentation [16] with a Dice score greater than 90%, cotton leaf segmentation [24] with a correct rate of 98%, and vegetable leaf segmentation [25]. The random walker algorithm was used in an interactive tool for leaf annotation [26] with a Dice of 97%. The robust random walker algorithm was proposed in [27] for leaf segmentation under different conditions with an F-measure of 98%. The K-Means algorithm was used for extraction of paddy leaf images for detecting leaf disease [12], conducting tomato leaf segmentation [28] with an F1 score of 98%, and detecting defective regions in leaves [29]. Deep learning was used in [2] for leaf counting. Mask R-CNN was used in [10] for leaf segmentation with a misclassification error of 1% and classification against complex backgrounds. A convolutional neural network (CNN) was used to detect and recognize leaf diseases [13]. Leaf segmentation based on color indexes, including normalized difference index (NDI), excess green minus excess red index (ExGR), and color index of vegetation extraction (CIVE), was addressed in [30] with segmentation rates of 80.7%, 80.9%, and 81.4%, respectively. Even though the previously described leaf segmentation techniques showed a high performance, they used a visible camera (RGB camera). Thus, they worked in the daytime. Furthermore, most of them were not real-time systems. Other cameras, such as an infrared camera, are also employed. The NoIR camera, which is a standard camera without the infrared filter, thus allowing the infrared spectrum (around 880 nm) to be captured by the sensor, was used in [3; 4; 31; 32]. A near-infrared (NIR) camera was used in [5] for leaf phenotyping. A stereo infrared camera that worked in the NIR spectrum (700 nm to 1400 nm) was used in [7] for leaf growth modeling. To the best of our knowledge, no such works have used a thermal camera for leaf segmentation. The typical applications of a thermal camera in plant monitoring are for plant canopy temperature measurement [33; 34; 35; 36], plant water status monitoring [34; 37], and leaf stomata conductance measurement [14; 38]. A technique related to leaf segmentation was proposed by [36], in which a visible camera is combined with a thermal camera. In that system, the visible camera was used for leaf segmentation in order to define the region for temperature measurement by the thermal camera.