## 1 Introduction  
Understanding the current state of soil is critical for appropriate crop management. Climate change has a major impact on the agricultural soil environment [1]. Frequent droughts and floods caused by climate change bring about drastic changes in soil moisture and density. Soil moisture and soil dry density are factors that have a significant impact on crop growth. Because evapotranspiration of crops varies according to soil moisture, it has a great influence on crop growth [2]. Additionally, soil structure and compaction affect crop growth [3]. Soil properties' measurement methods can be divided into experimental methods and sensors. Soil management using sensors is particularly widely used in the field of precision agriculture [4]. Soil density is measured using an in situ density test. Current soil moisture and density measurement methods are time-consuming and costly, limiting their widespread use [5]. Remote sensing has brought advances in soil management. Remote sensing has a great advantage because it can stably acquire data over a wide area [5]. However, since remote sensing data represent a wide area, it is difficult to use it for detailed field management. Recently, much research has been conducted on predicting soil properties using digital imagery to rapidly determine the current condition of soils. Detailed digital images acquired through precise instruments such as SEM and X-ray tomography make an extensive analysis of the soil feasible. However, these instruments are time- and cost-consuming for use in agricultural lands where numerous analyses are required. Therefore, there is a need to develop a method for determining the current condition of soil via a commercial-level digital camera. Deep learning is a machine learning technique that trains computers to learn through multiple examples in the same manner that people do naturally. At this point, the machine mimics humans by discovering a certain pattern in various pieces of information [6]. Deep learning had issues in the past with poor accuracy and a prolonged training period, but more recent technical advancements have addressed these issues by accelerating computation and creating new algorithms, and it is now being used for analysis in several industries. In particular, processing image data sets has traditionally been complex and time-consuming, but as of recently, analysis can be completed quickly due to high-speed computing using a computer's graphics processing unit (GPU). In the field of image classification, convolutional neural network (CNN), one of the deep learning technologies, has overcome the drawbacks of the pre-existing methodologies [7]. CNN was first introduced by LeCun in 1989 and achieved success in the field of writing recognition [8]. Later, CNNs were widely employed with the publication of LeNet [9]. [PERSON] et al. [10] developed AlexNet, a fundamental CNN-based image classification system that uses a convolutional layer, pooling layer, and fully connected layer. The accuracy of CNN classification has been improved by the development of representative models, notably GoogleNet [11], ResNet [12], and DenseNet [13], which are widely used to handle various problems. Since the introduction of deep learning, numerous studies have attempted to evaluate soil properties using digital photographs and observed data. [PERSON] et al. [14] conducted a study using CNN to estimate the amounts of sand, pH, organic matter, calcium carbonate, and phosphorus. [PERSON] et al. [15] carried out a study using an extreme learning machine (ELM) to estimate the soil's sand content, pH, organic carbon content, phosphorus content, and calcium content. These two investigations used spectral information as input data for the model to predict various soil parameters, but they were unable to predict soil water content or soil dry density. Based on satellite remote-sensing data, [PERSON] et al. [16] forecasted crop production using a CNN and a deep Gaussian process. [PERSON] et al.