Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy

Abstract

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

Insights and Interpretation of the Trends for <em>in ovo</em> Sexing Technologies in Papers and Patents

“…The targets described in the state-of-the-art vary in 1) sexual-specific coloring (90-92), 2) embryonic growth rate (93), 3) sex-related blood absorption (94-96), 4) heart rate or body movement (97-100), 5) egg yolk ratio (101), 6) egg photoluminescence (102), and 7) sex determining spectral features on the germinal disk or other regions in the egg (103)(104)(105)(106)(107)(108)(109)(110)(111)(112)(113).The targets described in the state-of-the-art vary in 1) sexual-specific coloring (90-92), 2) embryonic growth rate (93), 3) sex-related blood absorption (94-96), 4) heart rate or body movement (97-100), 5) egg yolk ratio (101), 6) egg photoluminescence (102), and 7) sex determining spectral features on the germinal disk or other regions in the egg (103)(104)(105)(106)(107)(108)(109)(110)(111)(112)(113).…”

Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion

“…Hyperion hyperspectral satellite images from Maharashtra, India, were used to classify five different soil types. Li et al 20 aimed to find the applicability of the CNN algorithm for land cover classification with small samples from six different places in Qingdao, China. Spectra of soil samples were collected using a spectrometer.…”

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry

“…25 breakthroughs in various fields of analytical chemistry, such as chromatography, 26,27 ion mobility spectrometry, 28,29 mass spectrometry, 30,31 nuclear magnetic resonance (NMR) spectroscopy, 32 Raman spectroscopy, 33−35 and infrared spectroscopy. 36,37 Specifically in the field of GC−MS, deep learning has been used for peak detection, 38 retention index prediction, 39,40 spectral library retrieval, 41 mass spectral prediction, 42,43 and overlapping peak resolution. 44−47 In 2023, Fan et al 47 proposed the AutoRes method based on the pseudo-Siamese convolutional neural networks (pSCNN).…”

“…With the proposal of network architectures such as convolutional neural networks (CNN), graph neural networks (GNN), recurrent neural networks (RNN), and attention-based networks, it has achieved many groundbreaking advances in computer vision, natural language processing, speech recognition, and artificial intelligence (AI) for science. , Deep learning is also widely used in analytical chemistry due to the availability of spectra, structures, and property databases . It has already made breakthroughs in various fields of analytical chemistry, such as chromatography, , ion mobility spectrometry, , mass spectrometry, , nuclear magnetic resonance (NMR) spectroscopy, Raman spectroscopy, − and infrared spectroscopy. , Specifically in the field of GC–MS, deep learning has been used for peak detection, retention index prediction, , spectral library retrieval, mass spectral prediction, , and overlapping peak resolution. − In 2023, Fan et al proposed the AutoRes method based on the pseudo-Siamese convolutional neural networks (pSCNN). It can fully automate the batch processing of untargeted GC–MS data, and the entire resolution process does not require any parameters to be optimized.…”