Chlorophyll fluorescence imaging for process optimisation in horticulture and fresh food production

Herppich, Werner B.

doi:10.32615/ps.2021.033

Cited by 6 publications

(4 citation statements)

References 135 publications

(181 reference statements)

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“…ChlF parameters can be used to access photosynthetic function and plant tolerance to environmental stresses [ 31 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ]. The method is low cost but highly sensitive, as well as rapid and non-destructive [ 31 , 59 , 85 , 86 , 87 , 88 , 89 , 90 ].…”

Section: Chlorophyll a Fluorescence Imaging Analysismentioning

confidence: 99%

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis

Moustaka

Moustakas

2023

Biosensors

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Most agricultural land, as a result of climate change, experiences severe stress that significantly reduces agricultural yields. Crop sensing by imaging techniques allows early-stage detection of biotic or abiotic stress to avoid damage and significant yield losses. Among the top certified imaging techniques for plant stress detection is chlorophyll a fluorescence imaging, which can evaluate spatiotemporal leaf changes, permitting the pre-symptomatic monitoring of plant physiological status long before any visible symptoms develop, allowing for high-throughput assessment. Here, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress. Chlorophyll a is able to detect biotic stress as early as 15 min after Spodoptera exigua feeding, or 30 min after Botrytis cinerea application on tomato plants, or on the onset of water-deficit stress, and thus has potential for early stress detection. Chlorophyll fluorescence (ChlF) analysis is a rapid, non-invasive, easy to perform, low-cost, and highly sensitive method that can estimate photosynthetic performance and detect the influence of diverse stresses on plants. In terms of ChlF parameters, the fraction of open photosystem II (PSII) reaction centers (qp) can be used for early stress detection, since it has been found in many recent studies to be the most accurate and appropriate indicator for ChlF-based screening of the impact of environmental stress on plants.

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Section: Chlorophyll a Fluorescence Imaging Analysismentioning

confidence: 99%

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis

Moustaka

Moustakas

2023

Biosensors

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“…Furthermore, from the above basic parameters, also the photochemical quenching coefficient (q P = (F m − F t )/(F m − F 0 )), the non-photochemical quenching (NPQ = (F m − F m )/F m ), and the quantum yields of regulated (Y NPQ = F t /F m −F t /F m ) and non-regulated non-photochemical energy loss in PSII (Y NO = F t /F m ) were calculated for each PPFR step (c.f. [35,58]).…”

Section: Chlorophyll-fluorescence Analysis-rapid Light Curvesmentioning

confidence: 99%

Comprehensive Assessment of the Dynamics of Banana Chilling Injury by Advanced Optical Techniques

Herppich

Zsom

2021

Applied Sciences

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Green-ripe banana fruit are sensitive to chilling injury (CI) and, thus, prone to postharvest quality losses. Early detection of CI facilitates quality maintenance and extends shelf life. CI affects all metabolic levels, with membranes and, consequently, photosynthesis being primary targets. Optical techniques such as chlorophyll a fluorescence analysis (CFA) and spectroscopy are promising tools to evaluate CI effects in photosynthetically active produce. Results obtained on bananas are, however, largely equivocal. This results from the lack of a rigorous evaluation of chilling impacts on the various aspects of photosynthesis. Continuous and modulated CFA and imaging (CFI), and VIS remission spectroscopy (VRS) were concomitantly applied to noninvasively and comprehensively monitor photosynthetically relevant effects of low temperatures (5 °C, 10 °C, 11.5 °C and 13 °C). Detailed analyses of chilling-related variations in photosynthetic activity and photoprotection, and in contents of relevant pigments in green-ripe bananas, helped to better understand the physiological changes occurring during CI, highlighting that distinct CFA and VRS parameters comprehensively reflect various effects of chilling on fruit photosynthesis. They revealed why not all CFA parameters can be applied meaningfully for early detection of chilling effects. This study provides relevant requisites for improving CI monitoring and prediction.

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“…Chlorophyll fluorescence imaging was a visual technology in chlorophyll fluorescence technology, which could reflect the photosynthesis information of crops such as the absorption and conversion of light energy by leaves, the transmission and distribution of energy, and the state of reaction centers [19]- [21]. Chlorophyll fluorescence imaging technology could obtain the CFP and the spatial heterogeneity distribution of chlorophyll fluorescence non-destructively, simply and quickly, and had become an important method for diagnosing chlorophyll fluorescence [22]- [24]. Many scholars used chlorophyll fluorescence imaging to study the effects of stress on plants.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

Yan

2022

IEEE Access

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Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress. INDEX TERMSChlorophyll fluorescence imaging, drought stress, feature extraction, machine learning, tomato seedlings. YAN LONG received the B.S. degree in electronic and information engineering and the M.S. degree in communication engineering from Shandong University, China, in 2005 and 2008, respectively, and the Ph.D. degree in agricultural electrification and automation from Northwest A&F University, China, in 2015. Since 2018, she has been an Associate Professor with the Mechanical and Electronic Engineering Department, Northwest A&F University. Her research interests include digital agriculture and agricultural information engineering, agricultural electronics, and automation technology. MINJUAN MA received the B.S. degree in electronic information engineering from Northwest A&F University, China, in 2019, where she is currently pursuing the M.S. degree in agricultural electrification and automation. Her research interests include digital agriculture and agricultural information engineering.

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Chlorophyll fluorescence imaging for process optimisation in horticulture and fresh food production

Cited by 6 publications

References 135 publications

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis

Comprehensive Assessment of the Dynamics of Banana Chilling Injury by Advanced Optical Techniques

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

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