# Development of Potato Mass Estimation System Based on Deep Learning
## 1 Introduction  
Potato is one of the world's four major food crops along with rice, wheat, and corn, and its estimated worldwide production was 356 million tons and its estimated cultivation area was 1.649 million hectares in 2020 [1]. In the case of Korea, its production was 550 thousand tons and its estimated cultivation area was 23,599 hectares in 2020, making it one of the field crops that represent major income sources for farmhouses [2]. Recently, the cost of labor in Korea has been increasing due to a decrease in the rural population and the aging of the population, thereby increasing the production costs of agricultural products [3]. Accordingly, precision agriculture--which reduces production costs while simultaneously increasing production--is becoming increasingly important. The objective of precision agriculture is to minimize input resources and increase production to fit the characteristics of each location by monitoring the state, environment, and conditions of farmland and crops and combining the result with the location information obtained by GPS [4]. The concept of precision agriculture was first established in 1929 along with the study on the measurement of the spatial variability of soil acidity carried out by [PERSON] and [PERSON] of Illinois University in 1929 [5, 6], but the concept began to be studied in earnest with the commercialization of GPS and GIS technologies in the middle of the 1990s. Recently, in addition to being a solution for reducing the labor force, precision agriculture has been attracting attention as an alternative that can be used to minimize environmental pollution while using minimal input materials. In addition, interest in precision agricultureis also increasing as a result of sensor commercialization and price stabilization along with advancements in the technologies used. The yield variability information of a field is essential information that can be used as a basis for understanding the cultivation result of the current year and establishing the farming plan for the next year. Further, a yield map created by combining yield information and GIS technology can be of a great help to farmhouse business management as it can allow for the yield information of the farmland to be intuitively determined. For the measurement of the yield information, a method using load cells and machine vision has been studied extensively [7; 8; 9; 10; 11]. Load cells are most widely used for yield monitoring, and in the case of crops such as rice and wheat, systems using load cells attached inside a combine harvester have been commercialized in the USA, Europe, Japan, and so on. These crops can be accurately monitored by separating stems and products through post-harvest threshing, but it is difficult to use the same method for potatoes that are dug from a field and mixed with soil, stones, and stems in conveyor belts. For potatoes, studies have been carried out using machine vision, which is less constrained by space and can be configured in a relatively simple manner [12]. [PERSON] [13] attempted to measure potato sizes using the pixel count of a projected area by installing a CCD camera at the end of a conveyor belt, and [PERSON] and [PERSON] [14] developed a large-quantity estimation method using a line scan camera. [PERSON] et al. [15] measured the volume of potatoes by calculating depth information using stereo vision, and [PERSON] [12] conducted a potato weight estimation study using a regression equation and the images of potatoes dug from a field. However, there was a problem in that the same overlapping potatoes had to be removed from continuously taken images and the potatoes located in the boundary part had to be excluded. Artificial intelligence, which is attracting attention in a wide variety of industries, can present a solution to such a problem. Artificial neural networks that are used to detect objects include RCNN (region with convolutional network), fast RCNN, and faster RCNN.