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Plants
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The Application of Spectral Techniques in Agriculture and Forestry—3rd Edition
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The Application of Spectral Techniques in Agriculture and Forestry—3rd Edition

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1013

Special Issue Editor

Prof. Dr. Youzhen Xiang

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling, Xianyang 712100, China
Interests: smart irrigation; efficient use of crop water and fertilizer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of spectroscopic techniques in the fields of agriculture and forestry has emerged as a focal point of research and, consequently, this Special Issue is dedicated to exploring innovative applications of spectroscopic techniques in these domains, particularly with regard to proximal and remote scales.

The non-invasive nature and high sensitivity of these technologies render them an ideal choice for the study of plant ecosystems. Through spectroscopic techniques, we gain profound insights into the physiological status, growth processes, and environmental adaptability of crops and forest vegetation. From monitoring plant health to soil analysis, and from assessing water quality assessment to the monitoring of forest ecosystems, spectroscopic technologies provide an abundance of data, facilitating precision agriculture and sustainable forestry management.

This Special Issue therefore warmly welcomes original research articles, reviews, and brief communications that focus on the fundamental and applied research of spectroscopic techniques in the analysis and sensing of crop and plant systems. We eagerly anticipate contributions (ranging from laboratory to field settings and from proximal to remote scales) that foster the continuous innovation of spectroscopic technologies in the fields of agriculture and forestry.

Prof. Dr. Youzhen Xiang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Plants 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 2700 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

  • thermal infrared imaging
  • unmanned aerial vehicles (UAVs)
  • multispectral
  • hyperspectral
  • plants
  • crops
  • forestry
  • remote sensing
  • precision agriculture

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Published Papers (1 paper)

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Research

29 pages, 6843 KB  
Article
VIS–NIR–SWIR Hyperspectral Imaging and Advanced Machine and Deep Learning Algorithms for a Controlled Benchmark of Bean Seed Identification and Classification
by Renan Falcioni, Nicole Ghinzelli Vedana, Caio Almeida de Oliveira, João Vitor Ferreira Gonçalves, Marcelo Luiz Chicati, José Alexandre M. Demattê and Marcos Rafael Nanni
Plants 2026, 15(6), 933; https://doi.org/10.3390/plants15060933 - 18 Mar 2026
Viewed by 792
Abstract
Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; [...] Read more.
Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; 100 seeds per accession), comprising 30 common bean (Phaseolus vulgaris L.) landraces plus two outgroup legumes (Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (n = 2240) reserved for fully independent training and 30 seeds per accession (n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R2 of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54–456.30 nm and 577.02–597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—3rd Edition)
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