# Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
## 1 Introduction  
Corn is one of the largest crops grown around the world due to its application in food, feed, chemical engineering, and energy [1; 2; 3]. Mechanization and automation for corn harvest have been fully developed in the last century. In America, Oceania, and Europe, corn kernels are usually obtained directly through harvest process using grain harvesters or combined grain-stover harvesters [4]. However, for some growing regions in Asia, the moisture is too large for kernel harvest, as it would induce unacceptable kernel damage [5]. Hence, the segmented harvesting is widely applied in such regions [6]. Specifically, corn ears are snapped firstly in the farmland by a harvester, as shown in Figure 0(a). Then, corn ears are dried until the moisture content of kernels are suitable for threshing. However, as shown in Figure 0(b), corn ears are inevitably damaged by the peeling devices. Ear damage means the corn kernels drop from ears during the peeling process, and the dropped kernels fail to be harvested, leading to considerable harvest loss. Preliminary studies have demonstrated corn ear damage is mainly affected by the rotation speed, surface structure, and material and installation clearance of snapping roller and transmission blade [7; 8]. Researchers believe adjusting these working parameters can effectively reduce corn ear damage [9; 10]. However, the adjusting operation cannot be executed without efficient and accurate recognition of corn ear damage. In other words, recognition is the technical prerequisite to reduce damage. Therefore, it is necessary to develop an automatic, non-contact, non-destructive, and rapid method for corn ear damage recognition. Image processing methods have been attracting increasing attentions in agriculture products non-destructive detection [11; 12]. Depending on the type of information, such methods can be divided into hyperspectral image (HSI) processing methods and nature image processing methods [13; 14], HSIs comprise dozens or hundreds of narrow spectral bands with high spatial resolution, such that they are able to be used for recognition of objects [14]. Due to its great volume and high cost, HSI recognition is frequently employed for agriculture products detection in post-harvest. For instance, [PERSON] et al. developed a compact machine vision system to detect aflatoxin in chilli peppers based on HSIs and machine learning [15]. [PERSON] et al. utilized HSIs to identify water and nitrogen availability for rainfed corn crops [16]. Nature images are acquired by normal camera [17; 18; 19]. RGB images are the typical form of nature images [20; 21]. Comparing to HSIs, RGB images are more suitable for online agricultural detection as its lower computational complexity and cost. For the field of seeding, [PERSON] et al. developed a machine-vision-based mechanism to measure position of seed drills relative to the previous lines, which is used in a feedback control loop [22]. [PERSON] et al. utilized high-speed camera system to measure the seed drill seed spacing and velocity of fall of seeds [23]. [PERSON] et al. proposed an image processing algorithm to detect the performance of seed-sowing devices, including the the breadth of the seed array, the coordinates of the seed array center, the distance between seed arrays, and seed intervals and non-seed intervals of each seed array [24]. For the filed of harvesting, [PERSON] et al. used unmanned aerial vehicle to acquire nature images for agricultural harvesting equipment route planning and harvest volume measuring [25]. [PERSON] et al. developed an image processing system to guide automatic harvesting of oranges [26]. [PERSON] et. al proposed an image preprocessing method for night vision of apple harvesting robot [27]. For the field of corn ear harvest, damage forms are various in different processes, such as whole ear broken, kernel damage, and peeling ear damage. In ear snapping process, a whole ear may be broken off by headers. To address this issue, [PERSON] et al.