## 1 Introduction  
In South Korea, the need for automation of physically demanding agricultural labor is increasing because of the aging agricultural workforce and the growing proportion of women among the agricultural workforce [1]. In agricultural work such as weeding and pruning, the technology to distinguish and classify plant parts is necessary for automation of various agricultural tasks. Classification of plant parts can be performed using an image recognition technology. Once an image of a recognition target is obtained, pre-processing to extract the interested region is performed. The process of recognizing an object and transforming it into a suitable format to be processed with a computer is then performed by dividing the features of the recognition target within the area of interest [2]. Machine vision is a useful tool for plant recognition and identification [3]. To extract leaves and canopies, various algorithms such as Active Shape Model [4], Color Segmentation [5] using an RGB-D (Red, Green, Blue Depth) Camera, Support Vector Machines [6], Clustering Algorithm [7], Watershed Algorithm [8; 9; 10], and genetic algorithm [11] have been proposed and successfully used to recognize plant parts in the image. Machine recognition has been effectively used to analyze the shape and growth status of crops in fields such as plant phenomics. PhenoAIXpert (LemaTec, Aachen, Germany) is using machine recognition technology to acquire plant phenotypic information in an image-spectral acquisition chamber. In addition, PlantScreen (Photon Systems Instruments, Drasov, Czech Republic) acquires an image and spectral information of plants and uses machine recognition technology to select excellent plants based on plant shape and spectral response information. However, plant parts recognized via images have been acquired in a controlled experimental environment rather than in the field where plants are actually grown. In the actual farm field, the canopy, including leaves, does not exist alone. A group of plants can form a colony to affect image recognition, making it difficult to clearly differentiate plant parts [12]. Various methods such as vegetation index [13] have been used to measure crop productivity on a farm and to diagnose crop growth. [PERSON] et al. (2016) conducted an experiment to confirm the water stress of soybeans through NDVI (Normalized Difference Vegetation Index) images and confirmed a significant correlation between water stress and the NDVI of soybeans from each experiment [14]. To introduce plant shape recognition technology through images, it is necessary to distinguish targeted plant parts from the surrounding background. In a real farm field, it is easy to misread the contrast of an object because of lighting, sunlight, and shadow, which significantly lower the recognition rate of an object [15]. To improve the recognition rate of an object in an image, various methods such as Bayesian-classifier [16] and Clustering [17] using machine learning have been proposed. Methods for improving the recognition rate of objects in images have been evaluated for their accuracies in a competition called ILSVRC (ImageNet Large Scale Visual Recognition Challenge) [18]. Prior to 2012, when machine learning was predominant, the average recognition rate was below 75%. In 2012, [PERSON] et al. [19] proposed a convolutional neural network (CNN), an artificial neural network model, and showed a recognition rate of 85%, exceeding the existing recognition rate. Since 2012, various artificial neural network models have been proposed, showing recognition accuracy exceeding 95%, which is comparable to human recognition accuracy [20]. An artificial neural network is a pattern recognition method inspired by the interconnection of neurons in the human nervous system. A general artificial neural network consists of an input layer, a hidden layer, and an output layer. Each layer includes neurons [21]. Neurons between neighboring layers are connected by weights. Input values are learned by changing weights through repeated learning.