###### Abstract  
This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape  
Optical equipment, such as satellites and digital cameras, can also be classified as sensors because they collect information about an object's wavelength or color. Sensory tests may appear somewhat irrational and unscientific, but they have been used for a long time and are still valuable in predicting soil properties. In feeling tests, the texture, color, and scent are used to identify soil properties. The Munsell chart is widely used for classifying forest soil and can be regarded as an example of a color-based feeling test [10]. In particular, soil texture can be predicted with a high level of accuracy through feeling tests [11; 12; 13]. Soil water content and bulk density can also be roughly estimated through feeling tests, but quantitative values can be difficult to obtain [14; 15].  
Digital image processing (DIP) is a technique used to extract useful information from digital images. This can be regarded as an extension of the feeling test using vision, in that information is obtained based on the distribution of color information present in a digital image [16]. Unlike qualitative and empirical feeling tests, DIP can provide quantitative and repeatable information [17; 18]. From digital images of soil, various information such as color, texture, and the distribution of soil particles and voids can be obtained [19; 20].  
Deep neural network (DNN) models are useful tools for analyzing complex relationships between input and output variables. These models can extract important features from large and complicated datasets, enabling them to understand the data on a higher level [21; 22]. DNN models are especially effective for analyzing datasets with nonlinear relationships between input and output variables. Furthermore, DNN models can be generalized to new data well, making them suitable for accurate predictions on previously unseen samples. There have been various attempts at using various deep learning models including DNN to predict soil properties. For instance, [PERSON] et al. [23] employed a total of 46 parameters in their model to predict crop selection and yield. Other studies have shown that it is possible to predict soil texture from images by extracting relevant features, as exemplified by [PERSON] et al. [24] and [PERSON] et al. [20]. Similarly, [PERSON] et al. [25] used a convolutional neural network to predict permeability based on images. These examples illustrate the potential of DNN models in analyzing complex relationships between input and output variables for a wide range of applications. Therefore, using a DNN regression model is appropriate for predicting soil water content and bulk density based on the extracted various features from soil surface images.  
The objectives of this study were to investigate the potential of a DNN regression model using in situ soil surface images for predicting soil water content and bulk density. A digital image acquisition device was fabricated to acquire in situ soil surface images under various soil water content and bulk density conditions. Through digital image processing, features that can express color, texture, and shape were extracted from soil surface images and used as input parameters for DNN regression models. Multiple regression analysis was performed to compare the performance with the DNN regression models.