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Plants
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Practical Applications of Chlorophyll Fluorescence Measurements
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Practical Applications of Chlorophyll Fluorescence Measurements

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 3245

Special Issue Editors

Prof. Dr. Hazem M. Kalaji

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Guest Editor
Department of Plant Physiology, Faculty of Agriculture and Biology, Warsaw University of Life Sciences SGGW, Warsaw, Poland
Interests: fluorescence sensors; chlorophyll fluorescence analysis; photochemistry of photosynthesis; plant stress; physiology of plants and algae; plant talk and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Piotr Dabrowski

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Guest Editor
Department of Environmental Improvement, Warsaw University of Life Sciences—SGGW, Warsaw, Poland
Interests: chlorophyll fluorescence; photosynthesis; plant stresses; physiology of plants and algae; green infrastructure; plants in urban areas
Special Issues, Collections and Topics in MDPI journals
Dr. Soni Vineet

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Guest Editor
Group Leader, Plant Bioenergetics & Biotech Laboratory, Department of Botany, Mohanlal Sukhadia University Udaipur, Rajasthan 313001, India
Interests: photosynthesis and chlorophyll a fluorescence analysis in plants; abiotic stress tolerance in plants; conservation of threatened plants; in vitro morphogenesis in plants

Special Issue Information

Dear Colleagues,

Currently, chlorophyll fluorescence measurement is regarded as one of the most promising tools in environmental, biological, agricultural, and horticultural sciences. The objective of this Special Issue of Plants is to gather impactful papers that are related to the practical application of chlorophyll fluorescence measurement across various domains, including plant sciences, ecosystem monitoring, industrial applications, and the commercial and civil sectors.

This technique’s utility in non-invasively monitoring the health and stress responses of plants makes it invaluable in sustainable agriculture practices and precision farming. By assessing how plants absorb light and convert it to energy, chlorophyll fluorescence can reveal much about a plant’s physiological state under different environmental conditions.

Further integration of chlorophyll fluorescence with Artificial Intelligence (AI) and Machine Learning (ML) technologies opens new avenues for advancements. AI and ML can enhance the interpretation of fluorescence data, providing more accurate assessments of plant health, predicting future growth patterns, and optimizing conditions for plant resilience. Moreover, the combination of chlorophyll fluorescence measurement with AI and ML can facilitate the creation of biological feedback systems that enable plants to control their growth environments.

Potential papers may cover, but are not limited to, the following topics:

  • The development of AI models that integrate chlorophyll fluorescence data to track plant health and productivity.
  • Machine Learning algorithms for predicting plant physiological status, diseases, and pests based on fluorescence signals.
  • The use of drone and satellite imaging to measure chlorophyll fluorescence at a large scale for ecosystem monitoring or agricultural management.
  • Case studies on the use of chlorophyll fluorescence in agriculture and horticulture to improve plant breeding and reduce costs and waste.
  • Innovative approaches in hardware and software for enhancing the sensitivity and accuracy of chlorophyll fluorescence measurements.

This Special Issue aims to showcase research that exemplifies the intersection of chlorophyll fluorescence with cutting-edge computational technologies, highlighting the technique’s potential to revolutionize fields from bio-monitoring to commercial agriculture. Contributions that demonstrate novel applications, particularly those that bridge traditional scientific boundaries, are especially welcomed.

Prof. Dr. Hazem M. Kalaji
Dr. Piotr Dabrowski
Dr. Soni Vineet
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. 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

  • abiotic and biotic stresses
  • agricultural management
  • artificial intelligence (AI)
  • biological feedback systems
  • chlorophyll fluorescence measurement
  • ecosystem monitoring
  • machine learning (ML)
  • plant health
  • precision farming
  • sustainable agriculture
  • technological integration

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Published Papers (2 papers)

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Research

15 pages, 6283 KiB  
Article
Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging
by Yixin Deng, Nan Xin, Longgang Zhao, Hongtao Shi, Limiao Deng, Zhongzhi Han and Guangxia Wu
Plants 2024, 13(15), 2089; https://doi.org/10.3390/plants13152089 - 27 Jul 2024
Viewed by 988
Abstract
Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional [...] Read more.
Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management. Full article
(This article belongs to the Special Issue Practical Applications of Chlorophyll Fluorescence Measurements)
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16 pages, 2034 KiB  
Article
Photosynthetic Performance and Yield Losses of Winter Rapeseed (Brassica napus L. var. napus) Caused by Simulated Hail
by Piotr Dąbrowski, Łukasz Jełowicki, Zuzanna M. Jaszczuk, Olena Kryvoviaz and Hazem M. Kalaji
Plants 2024, 13(13), 1785; https://doi.org/10.3390/plants13131785 - 27 Jun 2024
Viewed by 1564
Abstract
Winter oilseed rape (Brassica napus L.), Europe’s foremost oilseed crop, is significantly impacted by hailstorms, leading to substantial yield reductions that are difficult to predict and measure using conventional methods. This research aimed to assess the effectiveness of photosynthetic efficiency analysis for [...] Read more.
Winter oilseed rape (Brassica napus L.), Europe’s foremost oilseed crop, is significantly impacted by hailstorms, leading to substantial yield reductions that are difficult to predict and measure using conventional methods. This research aimed to assess the effectiveness of photosynthetic efficiency analysis for predicting yield loss in winter rapeseed subjected to hail exposure. The aim was to pinpoint the chlorophyll fluorescence parameters most affected by hail stress and identify those that could act as non-invasive biomarkers of yield loss. The study was conducted in partially controlled conditions (greenhouse). Stress was induced in the plants by firing plastic balls with a 6 mm diameter at them using a pneumatic device, which launched the projectiles at speeds of several tens of meters per second. Measurements of both continuous-excitation and pulse-modulated-amplitude chlorophyll fluorescence were engaged to highlight the sensitivity of the induction curve and related parameters to hail stress. Our research uncovered that some parameters such as Fs, Fm’, ΦPSII, ETR, Fo, Fv/Fm, and Fv/Fo measured eight days after the application of stress had a strong correlation with final yield, thus laying the groundwork for the creation of new practical protocols in agriculture and the insurance industry to accurately forecast damage to rapeseed crops due to hail stress. Full article
(This article belongs to the Special Issue Practical Applications of Chlorophyll Fluorescence Measurements)
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