Soil-Surface-Image-Feature-Based Rapid Prediction of Soil Water Content and Bulk Density Using a Deep Neural Network

نویسندگان

چکیده

This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of based on features extracted from in situ surface images. Soil images were acquired using Canon EOS 100d camera. The camera was installed vertical direction above layer. To maintain uniform illumination conditions, dark room LED lighting utilized. Following acquisition images, samples collected metal cylinder obtain measurements density. Various including color, texture, shape features, used as inputs both multiple regression analysis model. results show that can predict with root mean squared error 1.52% 0.78 kN/m3. outperformed analysis, achieving high accuracy These findings suggest combined learning techniques, provide fast reliable method important properties.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Surface Tension Prediction of Hydrocarbon Mixtures Using Artificial Neural Network

In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...

متن کامل

Prediction of Soil Salinity Using Neural Network and Multivariate Regression Based on Remote Sensing Indices and Comparison: A Case Study of Qazvin plain's Salt Marsh

Introduction: The spatial and temporal distribution of salts in the soil, the great extent of the Iranian deserts, and the adverse climatic conditions prevailing over them make it difficult to accurately determine the parameters and field measurements in some cases. In the last two decades, the use of field techniques and their combination with remote sensing data has contributed significantly ...

متن کامل

Soil Water Content Monitoring Using Electromagnetic Induction

The use of electromagnetic ~EM! induction measurements was evaluated to predict water content in the upper 1.50 m of a prototype engineered barrier soil profile designed for waste containment. Water content was monitored with a neutron probe, and bulk soil electrical conductivity was monitored with a Geonics EM38 ground conductivity meter at ten locations at approximately monthly intervals over...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13074430