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

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 451

Special Issue Editors

Dr. George Bazar

E-Mail Website
Guest Editor
Correltech Laboratory, ADEXGO Kft., 1222 Budapest, 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,

Following the success of the first Agriculture Special Issue on "The Application of Near-Infrared Spectroscopy in Agriculture", a second edition with the same subject, editorial team, and submission process is being launched by the Editorial Office.

After first appearing in agriculture, near-infrared spectroscopy has expanded to many other scientific and industrial fields, especially after the middle of the 20th century, when Karl Norris at the USDA and his pioneer fellows around the world laid down the principles of this technology. Due to the recent developments in hardware and software technologies, NIR technology is now routinely used even by non-specialists in a wide variety of applications. As it is often used in agriculture without or with few highly complex natural samples, it is very important to learn about the effects of the various factors influencing its performance, including sampling, sample preparation, and measurement presentation, physical and chemical matrices, spectrometer technology, data pretreatment and evaluation methods, interpretation of the results, spectroscopic relevance of the targeted estimate, or user care at any stage.

This Special Issue will collect studies discussing NIR spectroscopy’s recent development for agricultural product characterization 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 favoured. Comparisons with other non-targeted, targeted, rapid, or classical analytical approaches will be acknowledged.

You may view more information about our Joint Special Issue in Agronomy.

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. Agriculture is an international peer-reviewed open access semimonthly 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

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Published Papers (1 paper)

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Research

16 pages, 4641 KiB  
Article
A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle
by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang and Lingwei Liang
Agriculture 2025, 15(3), 307; https://doi.org/10.3390/agriculture15030307 - 30 Jan 2025
Viewed by 305
Abstract
The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose [...] Read more.
The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. Firstly, the preprocessing method of spectral characteristic bands combined with Savitzky‒Golay (SG) + Standard Normal Variate (SNV) was determined. Subsequently, all sample data were divided into six groups: N-deficient, K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess. The aforementioned models were then used for classification prediction. The results show that RF and CNN + LSTM models demonstrated good predictive performance. Specifically, RF achieved accuracy rates of 70.14%, 90.81%, 88.59%, and 85.37% in the classification tasks of Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. The CNN + LSTM model achieved accuracy rates of 93.33%, 63.33%, 99.2%, 83.33%, and 98.52% in the classification tasks of K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. Finally, in the Leave-One-Group-Out Validation (LOGOV) for validating the model’s generalisation performance, RF performed better in the N-deficient, K-deficient, and Ca-deficient tasks, achieving diagnostic accuracy rates of 80.19%, 81.43%, and 77.02%, respectively. The CNN + LSTM model showed a diagnostic accuracy rate of 66.72% in the N-excess classification task. The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios. Full article
(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture—2nd Edition)
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