Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.6
4.1
Journals
Plants
Special Issues
Application of Optical and Imaging Systems to Plants
share announcement

Application of Optical and Imaging Systems to Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3021

Special Issue Editor

Dr. Massimo Rippa

E-Mail Website
Guest Editor
Institute of Applied Sciences and Intelligent Systems “E. Caianiello” of CNR, 80072 Pozzuoli, Italy
Interests: infrared imaging for precision agriculture; thermography for NDT; optical and imaging systems; spectral characterizations; nanostructures for sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the application of optical and imaging systems has revolutionized plant science, providing researchers and scholars with powerful tools to explore the structure, function, and behavior of plants and crops. These technologies have become essential in addressing critical challenges in agriculture, plant biology, and environmental monitoring. By enabling precise, non-invasive, and high-throughput measurements, optical and imaging techniques facilitate a deeper understanding of plant physiology, stress responses, growth patterns, and interactions with the environment. The development of advanced optical systems, including hyperspectral imaging, infrared thermography, Raman spectroscopy, and lidar, has opened new possibilities for detecting subtle changes in plant health, diagnosing diseases, and monitoring crop yields. These tools also play a pivotal role in precision agriculture, where real-time data acquisition and analysis drive sustainable farming practices and optimize resource use.

This Special Issue aims to highlight the latest advancements, innovations, and applications of these technologies in the study of plants and crops. By fostering interdisciplinary collaboration among researchers in optics, imaging, and plant sciences, this issue seeks to inspire novel approaches to tackling global challenges such as sustainable agriculture, food security, and ecosystem preservation. We welcome original articles, reviews, and short communications focusing on fundamental or applied research related to the use of optical, spectrophotometric, and imaging techniques for the analysis, monitoring and sensing of plants and crops. We also seek contributions pertaining to applications in passive or active approaches, realized on the proximal or remote scale (e.g., with UAV systems), in laboratory or field studies. Additionally, we aim to cover spectral scales from UV to LWIR through multi- and hyperspectral applications.

Dr. Massimo Rippa
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

  • optical systems
  • spectrophotometry
  • infrared imaging
  • thermography
  • active thermography
  • passive thermography
  • sensing
  • UV radiation
  • plants
  • crops
  • remote sensing
  • proximal sensing
  • precision agriculture
  • multispectral imaging
  • hyperspectral imaging

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 6206 KB  
Article
Variation in Assessment of Leaf Pigment Content from Vegetation Indices Caused by Positions and Widths of Spectral Channels
by Alexander Machikhin, Anastasia Zolotukhina, Georgiy Nesterov, Daria Zdarova, Anastasia Guryleva, Oksana Gusarova, Sergei Ladan and Vladislav Batshev
Plants 2025, 14(21), 3355; https://doi.org/10.3390/plants14213355 - 31 Oct 2025
Viewed by 497
Abstract
Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment [...] Read more.
Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula. Pigment content was measured in leaves of Lactuca sativa L. and Cucumis sativus L. at contrasting developmental stages using VI-based reflection spectroscopy across the 450–950 nm spectral range with various protocols and spectrophotometry as the reference method. VI values were calculated with varying central wavelength and widths of spectral bands, and across different VI formulas. Comparative analysis of the obtained measurements revealed that even minor shifts in central wavelengths of less than 20 nm or the use of an alternative index formula could lead to relative errors of 42–77% in the estimation of chlorophylls and carotenoids content, while changes in bandwidth had a much smaller impact, resulting in only 2–5% relative errors. Even with identical parameters of spectral channels, the choice of an appropriate VI and its regression model could introduce significant errors, ranging from 36% to 86%. These findings highlight the critical role of instrument specifications and calibration models in the VIs-based method accuracy and stability, as measurement errors can lead to suboptimal agronomic decisions. Moreover, our study underscores that comparing results from different sensors or platforms can be unreliable unless the channel parameters and calibration details are clearly specified. Therefore, standardization and transparency in VIs assignment is vital to ensure reproducibility and cross-compatibility in non-destructive pigment monitoring by using various devices. Full article
(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)
►▼ Show Figures

Figure 1

20 pages, 3040 KB  
Article
Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning
by Snehit Vaddi, Thomas F. Burks, Zafar Iqbal, Pappu Kumar Yadav, Quentin Frederick, Satya Aakash Chowdary Obellaneni, Jianwei Qin, Moon Kim, Mark A. Ritenour, Jiuxu Zhang and Fartash Vasefi
Plants 2025, 14(21), 3352; https://doi.org/10.3390/plants14213352 - 31 Oct 2025
Viewed by 653
Abstract
The transmission of Escherichia coli through contaminated fruits and vegetables poses serious public health risks and has led to several national outbreaks in the USA. To enhance food safety, rapid and reliable detection of E. coli on produce is essential. This study evaluated [...] Read more.
The transmission of Escherichia coli through contaminated fruits and vegetables poses serious public health risks and has led to several national outbreaks in the USA. To enhance food safety, rapid and reliable detection of E. coli on produce is essential. This study evaluated the performance of the CSI-D+ system combined with deep learning for detecting varying concentrations of E. coli on citrus and spinach leaves. Eight levels of E. coli contamination, ranging from 0 to 108 colony-forming units (CFU)/mL, were inoculated onto the leaf surfaces. For each concentration level, 10 droplets were applied to 8 citrus and 12 spinach leaf samples (2 cm in diameter), and fluorescence images were captured. The images were then subdivided into quadrants, and several post-processing operations were applied to generate the final dataset, ensuring that each sample contained at least 2–3 droplets. Using this dataset, multiple deep learning (DL) models, including EfficientNetB7, ConvNeXtBase, and five YOLO11 variants (n, s, m, l, x), were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize the spatial responses of the models to bacterial presence. All YOLO11 models outperformed EfficientNetB7 and ConvNeXtBase. In particular, YOLO11s-cls was identified as the best-performing model, achieving average validation accuracies of 88.43% (citrus) and 92.03% (spinach), and average test accuracies of 85.93% (citrus) and 92.00% (spinach) at a 0.5 confidence threshold. This model demonstrated an inference speed of 0.011 s per image with a size of 11 MB. These findings indicate that fluorescence-based imaging combined with deep learning for rapid E. coli detection could support timely interventions to prevent contaminated produce from reaching consumers. Full article
(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)
►▼ Show Figures

Figure 1

22 pages, 2704 KB  
Article
Cross-Crop Transferability of Machine Learning Models for Early Stem Rust Detection in Wheat and Barley Using Hyperspectral Imaging
by Anton Terentev, Daria Kuznetsova, Alexander Fedotov, Olga Baranova and Danila Eremenko
Plants 2025, 14(21), 3265; https://doi.org/10.3390/plants14213265 - 25 Oct 2025
Viewed by 550
Abstract
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning [...] Read more.
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops. Full article
(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)
►▼ Show Figures

Figure 1

13 pages, 850 KB  
Article
Predictive Modeling of Lignocellulosic Content in Crop Straws Using NIR Spectroscopy
by Yifan Zhao, Yingying Zhu, Yumeng Ren, Yu Lu, Chunling Yu, Geng Chen, Yu Hong and Qian Liu
Plants 2025, 14(10), 1430; https://doi.org/10.3390/plants14101430 - 10 May 2025
Cited by 2 | Viewed by 900
Abstract
This study employs near-infrared spectroscopy (NIRS) combined with chemometrics to explore the feasibility and methodology for the rapid analysis of lignocellulosic content in straw. As the demand for biofuels and bioproducts increases, the efficient utilization of agricultural waste, such as straw, has become [...] Read more.
This study employs near-infrared spectroscopy (NIRS) combined with chemometrics to explore the feasibility and methodology for the rapid analysis of lignocellulosic content in straw. As the demand for biofuels and bioproducts increases, the efficient utilization of agricultural waste, such as straw, has become particularly important. Rapid analysis of lignocellulosic content helps improve the resource utilization efficiency of agricultural waste, providing significant support for biofuel production, agricultural waste valorization, and environmental protection. A total of 148 straw samples were used in this study, collected from Zhejiang, Jiangsu, and Heilongjiang provinces in China, covering rice straw (Oryza sativa L.), corn straw (Zea mays L.), wheat straw (Triticum aestivum L.), soybean straw (Glycine max L.), sorghum straw (Sorghum bicolor L.), rapeseed straw (Brassica napus L.), and peanut straw (Arachis hypogaea L.). After collection, the samples were first air-dried until surface moisture evaporated and then ground and sifted before being numbered and sealed for storage. To ensure the accuracy of the experimental results, all samples were subjected to a 6 h drying treatment at 60 °C before the experiment to ensure uniform moisture content. Partial least squares (PLS) and support vector machine (SVM) regression methods were employed for modeling analysis. The results showed that NIRS in combination with PLS modeling outperformed SVM in the calibration and prediction of lignocellulosic content. Specifically, the cellulose PLS model achieved a prediction set coefficient of determination (R2P) of 0.8983, root mean square error of prediction (RMSEP) of 0.6299, and residual predictive deviation (RPD) of 3.49. The hemicellulose PLS model had an R2P of 0.7639, RMSEP of 1.5800, and RPD of 2.11, while the lignin PLS model achieved an R2P of 0.7635, RMSEP of 0.6193, and RPD of 2.17. The results suggest that NIRS methods have broad prospects in the analysis of agricultural waste, particularly in applications related to biofuel production and the valorization of agricultural by-products. Full article
(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop