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The Application of Near-Infrared Spectroscopy in Agriculture

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 10137

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Dr. George Bazar

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Correltech Laboratory, ADEXGO Kft., 1222 Budapest, Hungary
Interests: near-infrared spectroscopy; electronic nose; multivariate data analysis; chemometrics; food quality
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Prof. Dr. Tamás Tóth

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Agricultural and Food Research Centre, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary
Interests: near-infrared spectroscopy; animal nutrition; feed technology; quality assessment; aroma sensing
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Dear Colleagues,

Near-infrared spectroscopy started its rise in agriculture. Since the middle of the 20^th century when Karl Norris at USDA and his pioneer fellows worldwide laid down the principals of the technology, it has spread to many other fields of science and industry. Due to the developments in hardware and software technology in recent years, NIR technology is now used routinely, even by non-specialists, in a wide variety of applications. As NIR spectroscopy is often used in agriculture without or with the minor preparation of highly complex natural samples, it is very important to gain knowledge about the effects of the various factors influencing the performance. These factors may include sampling, sample preparation and presentation to measurement, physical and chemical matrices, spectrometer technology, data pretreatment, data evaluation methods, results interpretation, the spectroscopic relevance of the targeted estimate, or the user’s care at any point.

This Special Issue aims to collect studies discussing the recent developments of NIR spectroscopy for the qualification of agricultural products at any point of the supply chain, from soil to feed and food. Studies summarizing experiences with novel sample matrices, hardware technologies, in-line or field applications, and data evaluation protocols are highly favored. Comparisons with other non-targeted or targeted, rapid or classical analytical approaches will be acknowledged.

You may choose our Joint Special Issue in Agriculture.

Dr. George Bazar
Prof. Dr. Tamás Tóth
Guest Editors

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Keywords

near-infrared spectroscopy
chemometrics
crop production
horticulture
animal husbandry
soil
feed
food
fruit
crop
animal product
milk
dairy
meat
egg
honey
quality assessment
process analytical technique

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Related Special Issue

The Application of Near-Infrared Spectroscopy in Agriculture—2nd Edition in Agronomy

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Research

17 pages, 6622 KiB

Open AccessArticle

A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning

by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu and Liying Cao

Agronomy 2024, 14(9), 2054; https://doi.org/10.3390/agronomy14092054 - 8 Sep 2024

Cited by 1 | Viewed by 893

Abstract

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model’s instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R²) on the test set are 4.577, 0.227, and 0.952, respectively. The average R² and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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15 pages, 2857 KiB

Open AccessArticle

Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning

by Haijun Qi, Bin Li, Jun Nie, Yizhi Luo, Yu Yuan and Xingxing Zhou

Agronomy 2024, 14(9), 1974; https://doi.org/10.3390/agronomy14091974 - 1 Sep 2024

Viewed by 545

Abstract

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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14 pages, 1787 KiB

Open AccessArticle

Analysis of Total Flavonoid Variation and Other Functional Substances in RILs of Tartary Buckwheat, with Near-Infrared Model Construction for Rapid Non-Destructive Detection

by Liwei Zhu, Qianxi Du, Taoxiong Shi, Juan Huang, Jiao Deng, Hongyou Li, Fang Cai and Qingfu Chen

Agronomy 2024, 14(8), 1826; https://doi.org/10.3390/agronomy14081826 - 19 Aug 2024

Viewed by 639

Abstract

According to the requirements of Tartary buckwheat breeding, it is necessary to develop a method for the rapid detection of functional substances in seeds. To ensure a diverse sample pool, we utilized the stable recombinant inbred lines (RILs) of Tartary buckwheat. The coefficients of variation of the total flavonoid, vitamin E (VE), and GABA contents of the RIL population were 15.06, 16.53, and 36.93, respectively. Subsequently, we established prediction models for the functional substance contents in Tartary buckwheat using near-infrared spectroscopy (NIRS) combined with chemometrics. The Kennard–Stone algorithm divided the dataset into training and test sets, employing six different methods for preprocessing spectra. The Competitive Adaptive Reweighted Sampling algorithm extracted the characteristic spectra. The best models for total flavonoid and VE were normalized using the first derivative. The calibration correlation coefficient (Rc) and prediction correlation coefficient (Rp) of the total flavonoid and VE prediction models were greater than 0.94. The optimal GABA prediction model underwent preprocessing via normalization combined with the standard normal variate, and the Rc and Rp values were greater than 0.93. The results demonstrated that the NIRS-based prediction model could satisfy the requirements for the rapid determination of total flavonoids, VE, and GABA in Tartary buckwheat seeds. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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21 pages, 27095 KiB

Open AccessArticle

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

by Chuanli Jiang, Jianyun Zhao and Guorong Li

Agronomy 2023, 13(11), 2816; https://doi.org/10.3390/agronomy13112816 - 15 Nov 2023

Cited by 3 | Viewed by 1884

Abstract

Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP₂O₅), total potassium oxide (TK₂O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R², RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R² = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP₂O₅, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK₂O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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17 pages, 4270 KiB

Open AccessArticle

Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection

by Siyao Yu, Haoran Bu, Xue Hu, Wancheng Dong and Lixin Zhang

Agronomy 2023, 13(8), 2120; https://doi.org/10.3390/agronomy13082120 - 13 Aug 2023

Cited by 4 | Viewed by 1609

Abstract

In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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19 pages, 4476 KiB

Open AccessArticle

The Nondestructive Model of Near-Infrared Spectroscopy with Different Pretreatment Transformation for Predicting “Dangshan” Pear Woolliness Disease

by Jiahui Zhang, Li Liu, Yuanfeng Chen, Yuan Rao, Xiaodan Zhang and Xiu Jin

Agronomy 2023, 13(5), 1420; https://doi.org/10.3390/agronomy13051420 - 20 May 2023

Cited by 6 | Viewed by 1874

Abstract

The “Dangshan” pear woolliness response is a physiological disease that mostly occurs in the pear growth process. The appearance of the disease is not obvious, and it is difficult to detect with the naked eye. Therefore, finding a way to quickly and nondestructively identify “Dangshan” pear woolliness disease is of great significance. In this paper, the near-infrared spectral (NIR) data of “Dangshan” pear samples were collected at 900–1700 nm reflectance spectra using a handheld miniature NIR spectrometer, and the data were modelled and analysed using random forest (RF), support vector machine (SVM) and boosting algorithms under the processing of 24 pretreatment methods. Considering the variations between different pretreatment methods, this work determined the relative optimality index of different pretreatment methods by evaluating their effects on model accuracy and Kappa and selected the best-performing first derivative with standard normal variate and Savitzky–Golay and first derivative with multiplicative scatter correction and Savitzky–Golay as the best pretreatment methods. With the best pretreatment method, all five models in the three categories showed good accuracy and stability after parameter debugging, with accuracy and F1 greater than 0.8 and Kappa floating at approximately 0.7, reflecting the good classification ability of the models and proving that near-infrared spectroscopy (NIRS) in the rapid identification of “Dangshan” pear woolliness response disease was feasible. By comparing the performance differences of the models before and after the pretreatment methods, it was found that the ensemble-learning models such as RF and boosting were more stringent on pretreatment methods in identifying “Dangshan” pear woolliness response disease than support vector machines, and the performance of the ensemble learning models was significantly improved under appropriate pretreatment methods. This experiment provided a relatively stable detection method for “Dangshan” pear woolliness response disease under nonideal detection conditions by analysing the impact of pretreatment methods and models on the prediction result. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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17 pages, 3834 KiB

Open AccessArticle

Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading

by Qiang Guan, Dongxue Zhao, Shuai Feng, Tongyu Xu, Haoriqin Wang and Kai Song

Agronomy 2023, 13(4), 1153; https://doi.org/10.3390/agronomy13041153 - 18 Apr 2023

Cited by 3 | Viewed by 1739

Abstract

Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)

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