自编码
淤泥
卷积神经网络
随机森林
土壤质地
人工智能
计算机科学
模式识别(心理学)
纹理(宇宙学)
深度学习
人工神经网络
土壤科学
环境科学
土壤水分
图像(数学)
地质学
古生物学
作者
Zhuan Zhao,Wenkang Feng,Jinrui Xiao,Xiaochu Liu,Shusheng Pan,Zhongwei Liang
出处
期刊:Agronomy
[MDPI AG]
日期:2022-12-03
卷期号:12 (12): 3063-3063
被引量:9
标识
DOI:10.3390/agronomy12123063
摘要
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 machine vision system using a deep learning autoencoder convolutional neural network random forest (DLAC-CNN-RF) model for soil texture identification. Different image features (color, particle, and texture) were extracted and randomly combined to predict sand, clay, and silt content via RF and DLAC-CNN-RF algorithms. The results show that the proposed DLAC-CNN-RF model has good performance. When the full features were extracted, a very high prediction accuracy for sand (R2 = 0.99), clay (R2 = 0.98), and silt (R2 = 0.98) was realized, which was higher than those frequently obtained by the KNN and VGG16-RF models. The possible mechanism was further discussed. Finally, a graphical user interface was designed and used to accurately predict soil types. This investigation showed that the proposed DLAC-CNN-RF model could be a promising solution to costly and time-consuming laboratory methods.
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