MOLECULES
·
2022
·Vol. 27
·期 6
·2017
·
IF 4.927
Zhuoyi Chen,Shijie Ren,Ruimiao Qin,Pengcheng Nie
Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH4-N), sodium nitrate (nitrate nitrogen, NO3-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH4-N, R2p= 0.92, RMSEP= 0.77% and RPD = 3.63; for NO3-N, R2p= 0.92, RMSEP= 0.74% and RPD = 4.17; for urea-N, R2p= 0.96, RMSEP= 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.
研究领域:
农学
·
土壤科学
·
光谱学
✓ 已验证
DOI
高影响因子
FOOD CHEMISTRY
·
2020
·
IF 6.306
Chu Zhang,Wenyan Wu,Lei Zhou,Huan Cheng,Xingqian Ye,Yong He
Black goji berry ( Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI.
研究领域:
食品科学
·
植物学
·
分析化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2021
·
IF 4.098
Jiyong Shi,Yueying Wang,Zhihua Li,Xiaowei Huang,Tingting Shen,Xiaobo Zou
In the early stage of P deficiency in cucumbers, the P deficiency symptoms in leaves are similar to the symptoms in control leaves at the early stage of aging and are difficult to identify with naked eyes or computer image processing techniques. In order to realize the quick diagnosis of P deficiency in plants at the early stage, the NIR hyperspectral images of control leaves and P-deficient leaves were collected, and the feature information of the NIR hyperspectral images was extracted by PCA and ICA respectively. Through PCA and HCA verification, the IC1 component diagram of P-deficient leaves NIR hyperspectral image could effectively characterize the features of invisible water-stained plaques caused by early P-deficient leaves. Region of interest from IC1 was selected to extract spectral information for classification, and the diagnostic rate was remarkably improved. Finally, 240 leaves were diagnosed by using the BP-ANN model with a diagnostic rate of 97.5%. In addition, the experiment verified that it was possible to diagnose whether the plant was in the state of P deficiency 21 days in advance, and timely guidance of top dressing was of great significance to increase yield.
研究领域:
植物学
·
农业科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
COMPUTERS AND ELECTRONICS IN AGRICULTURE
·
2021
·
IF 5.565
Bingquan Chu,Kai Chen,Xiaoxiao Pan,Qianying Wu,Shiwang Liu,Jinyan Gong,Xiaoli Li
Microalgae lipid and its degree of fatty acid unsaturation (DU) have the ability to undergo programmatic changes in response to different environmental and culture conditions. A real-time and reliable approach to monitor the change patterns of lipid-accumulation and DU is crucial for microalgal culture and bioprocess control. In this study, a fast visual and nondestructive method based on visible/short-wave near-infrared hyperspectral imaging (V/SNIR HSI) at 400–1000 nm region was developed to investigate the dynamic changes and content distribution of lipids and DU of Scenedesmus obliquus treated by nitrogen-low stress (0.3 g/L NaNO 3 , NL) and nitrogen-free stress (0 g/L NaNO 3 , NF). After collecting the average spectra from the region of interests (ROIs), the competitive adaptive reweighted sampling (CARS) algorithm and the successive projection algorithm (SPA) were applied to pick effective wavebands from the spectral data for the establishment of the lipid concentration and DU estimation models including partial least squares regression (PLSR) and multiple linear regression (MLR) . The prediction results show the CARS-PLSR (R p = 0.9762, RMSE p = 0.0143) and CARS-MLR (R p = 0.9477, RMSE p = 0.5718) performed the best for lipids and DU of S. obliquus , respectively. Based on the optimal models, the spatial distribution of lipid abundance and DU in liquid suspension were visualized, which could be helpful in exploring the dynamic process of lipid synthesis and DU of S. obliquus. The overall results show that V/SNIR HSI is a promising technique for fast and noninvasively monitoring the lipid-accumulation and its characteristics i.e. DU in situ, and is expected to be applied for efficient and high-quality lipid production in microalgae industries.
研究领域:
生物技术
·
环境科学
·
光谱学
·
微生物学
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）