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Agronomy
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The Application of Near-Infrared Spectroscopy in Agriculture
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The Application of Near-Infrared Spectroscopy in Agriculture

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5528

Special Issue Editors

Dr. George Bazar

E-Mail Website
Guest Editor
ADEXGO Kft. Correltech Laboratory, Lapostelki utca 13., 8230 Balatonfüred, Hungary
Interests: near-infrared spectroscopy; electronic nose; multivariate data analysis; chemometrics; food quality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tamás Tóth

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Near-infrared spectroscopy started its rise in agriculture. Since the middle of the 20th 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (4 papers)

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Research

21 pages, 27095 KiB  
Article
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
Viewed by 1122
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 [...] Read more.
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  
Article
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
Viewed by 1092
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 [...] Read more.
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  
Article
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 3 | Viewed by 1369
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 [...] Read more.
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  
Article
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 1283
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 [...] Read more.
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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