Abstract
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.
Funder
The National Natural Science Foundation of China
the Science and Technology Innovative Research Team Program in Higher Educational Universities of Guangdong Province
Special Research Projects in the Key Fields of Guangdong Higher Educational Universities
Natural Science Foundation of Guangdong Province
the Tertiary Education Scientific research project of Guangzhou Municipal Education Bureau
Guangzhou University Research Project
Subject
Agronomy and Crop Science
Reference32 articles.
1. Phogat, V.K., Tomar, V.S., and Dahiya, R. (2015). Soil physical properties. Soil Sci. Introd., 135–171.
2. Development of a novel machine vision procedure for rapid and non-contact measurement of soil moisture content;Mollazade;Measurement,2018
3. Klute, A. (1986). Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods, Soil Science Society of America.
4. A new method for the mechanical analysis of soils and other dispersions;Robinson;J. Agric. Sci.,1922
5. Comparison between grain-size analyses using laser diffraction and sedimentation methods;Ferro;Biosyst. Eng.,2010
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