# Tomato Maturity Estimation Using Deep Neural Network
## 2 Literature Review  
A convolutional neural network (CNN), which is the representative structure of deep learning, can detect objects effectively via hierarchical feature extraction [5]. CNN-based object detection has shown rapid progress in various fields, with visual recognition being the most spotlighted research area due to its similarity to human visual perception [6]. The architecture was extended to object-level detection [7] and pixel-level segmentation [8], and it was recently made possible to provide real-time level performance (YOLO) [9]. CNNs have been modified continuously to implement intelligent functions beyond object detection, and some researchers have generated a remarkable expansion in various fields, such as image generation [10,11] and context interpretation [12]. CNNs have been applied in the field of agriculture for fruit detection in robotic harvesting. [PERSON] et al. [13] detected tomato fruit along with its peduncle, in order to determine the exact cutting point. They used a YOLO model, which presented a detection accuracy of approximately 93% and a localization performance of 73.1 mAP (mean average precision). [PERSON] et al. [14] also detected tomato fruit based on ripeness using popular models such as YOLO and SSD. They reported that the YOLO-v4-based detection model had high precision (at 91%). CNN-based fruit detection has also presented performances that are high enough for practical use with sweet pepper [15], apple [16], and lychee [17]. In robotic harvesting, it is required that the detected fruit be harvested by estimating the maturity of the fruit. [PERSON] et al. [18] segmented mature green tomatoes using Mask R-CNN, and the results showed that the F1 score reached 92%. They reported that their method could be used to detect mature tomato robustly under various conditions, such as occlusion by other objects and similarly colored backgrounds, and that it could be used in a real environment. [PERSON] et al. [19] carried out the detection of ripe tomatoes in images captured from a real greenhouse. Their results show that the ripe tomatoes were detected successfully, even when tested with a challenging experimental setup in which simple inexpensive cameras were used. They stated that their method could be used practically in automatic harvesting. [PERSON] et al. [20] focused, in particular, on the classification of the level of tomato maturity. Color space analysis was performed to determine the various harvest times of mature tomato fruits and showed an accuracy of more than 90% in the classification of six stages of maturity (i.e., green, breaker, turning, pink, light red, and red). The studies mentioned above classified maturity into specific stages; however, they required a method that could evaluate maturity as a continuous value. This problem, referred to as label ambiguity, is similar to that of age estimation from facial images, and distribution learning has been proposed to address it [4]. Mean-variance loss is one of the approaches that allows this problem to be solved by learning the label distribution inner class, with the advantage of easy implementation by adding a mean-variance loss term into the model.