Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm

Soil determines the degree of water infiltration, crop nutrient absorption, and germination, which in turn affects crop yield and quality. For the efficient planting of agricultural products, the accurate identification of soil texture is necessary. This study proposed a flexible smartphone-based ma...

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Veröffentlicht in:	Agronomy (Basel) Jg. 12; H. 12; S. 3063
Hauptverfasser:	Zhao, Zhuan, Feng, Wenkang, Xiao, Jinrui, Liu, Xiaochu, Pan, Shusheng, Liang, Zhongwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2022
Schlagworte:	Accuracy Agricultural products Agriculture agronomy Algorithms Artificial neural networks Cameras Classification Clay color computer vision Crop yield Deep learning Digital technology DLAC-CNN-RF model Feature extraction Germination Graphical user interface identification Image processing Laboratory methods Machine learning Machine vision Methods Neural networks nutrient uptake Particle size Physical properties prediction Sand Silt silt fraction Smartphones Soil investigations Soil properties Soil texture Soil types Soils Spectrum analysis Texture user interface Vision systems Water infiltration Wavelet transforms China
ISSN:	2073-4395, 2073-4395
Online-Zugang:	Volltext
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