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.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13074430