scholarly journals Features and applications of a field imaging chlorophyll fluorometer to measure stress in agricultural plants

2020 ◽  
Author(s):  
Alexander I. Linn ◽  
Alexander K. Zeller ◽  
Erhard E. Pfündel ◽  
Roland Gerhards
Keyword(s):  
Chlorophyll Fluorescence ◽  
Plant Stress ◽  
Pulse Amplitude ◽  
Sensor Data ◽  
Stress Detection ◽  
Herbicide Resistant ◽  
Primary Stress ◽  
Field Estimation ◽  
Agricultural Plants ◽  
Imaging Sensor

Abstract Most non-destructive methods for plant stress detection do not measure the primary stress response but reactions of processes downstream of primary events. For instance, the chlorophyll fluorescence ratio Fv/Fm, which indicates the maximum quantum yield of photosystem II, can be employed to monitor stress originating elsewhere in the plant cell. This article describes the properties of a sensor to quantify herbicide and pathogen stress in agricultural plants for field applications by the Fv/Fm parameter. This dedicated sensor is highly mobile and measures images of pulse amplitude modulated (PAM) chlorophyll fluorescence. Special physical properties of the sensor are reported, and the range of its field applications is defined. In addition, detection of herbicide resistant weeds by employing an Fv/Fm-based classifier is described. The PAM-imaging sensor introduced here can provide in-field estimation of herbicide sensitivity in crops and weeds after herbicide treatment before any damage becomes visible. Limitations of the system and the use of a classifier to differentiate between stressed and non-stressed plants based on sensor data are presented. It is concluded that stress detection by the Fv/Fm parameter is suitable as an expert tool for decision making in crop management.

scholarly journals Global Trends of Usage of Chlorophyll Fluorescence and Projections for the Next Decade

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Alonso Zavafer ◽  
Leen Labeeuw ◽  
Cristian Mancilla
Keyword(s):  
Chlorophyll Fluorescence ◽  
Plant Stress ◽  
Pulse Amplitude ◽  
Pulse Amplitude Modulation ◽  
Global Trends ◽  
The Public ◽  
The Past ◽  
Scientific Opinion ◽  
Geographical Bias ◽  
Fast Rise

Chlorophyll fluorescence is the most widely used set of techniques to probe photosynthesis and plant stress. Its great versatility has given rise to different routine methods to study plants and algae. The three main technical platforms are pulse amplitude modulation (PAM), fast rise of chlorophyll fluorescence, and fast repetition rate. Solar-induced fluorescence (SIF) has also gained interest in the last few years. Works have compared their advantages and their underlying theory, with many arguments advanced as to which method is the most accurate and useful. To date, no data has assessed the exact magnitude of popularity and influence for each methodology. In this work, we have taken the bibliometrics of the past decade for each of the four platforms, have evaluated the public scientific opinion toward each method, and possibly identified a geographical bias. We used various metrics to assess influence and popularity for the four routine platforms compared in this study and found that, overall, PAM currently has the highest values, although the more recent SIF has increased in popularity rapidly during the last decade. This indicates that PAM is currently one of the fundamental tools in chlorophyll fluorescence.

In field identification of herbicide resistantApera spica-ventiusing chlorophyll fluorescence

2017 ◽  
Vol 8 (2) ◽  
pp. 283-287 ◽  
Author(s):  
P. Wang ◽  
G. G. Peteinatos ◽  
R. Gerhards
Keyword(s):  
Chlorophyll Fluorescence ◽  
Field Experiments ◽  
Visual Assessment ◽  
Sensor Data ◽  
Herbicide Application ◽  
Resistance Level ◽  
Ps Ii ◽  
Herbicide Resistant ◽  
Field Identification ◽  
Sensitive Population

The WEED-PAM®is a chlorophyll fluorescence sensor. It has already been applied in the detection of herbicide resistantAlopecurus mysuroidespopulations with promising results. Yet more work needs to be done in order to validate the system’s capabilities in different species. In this study, field experiments were conducted at three sites to clarify the capability of this sensor to detect herbicide resistantApera spica-ventipopulations. The plants were treated with five different herbicides: three ALS-, one ACCase- and one PS II- inhibitor. Five days after the herbicide treatment, sensor data were gathered. These data were compared with a visual assessment, performed 21 days after the herbicide application. The populations that exhibited a strong resistance to the ALS and PS II inhibitors could be differentiated from the sensitive ones. Yet theApera spica-ventipopulation with a low resistance level to the ACCase inhibitors cannot be differentiated from the sensitive population.

scholarly journals MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY

2016 ◽  
Vol XLI-B8 ◽  
pp. 911-913
Author(s):  
S. M. Irteza ◽  
J. E. Nichol
Keyword(s):  
Chlorophyll Fluorescence ◽  
Steady State ◽  
Satellite Images ◽  
Plant Stress ◽  
Fluorescence Signal ◽  
Absorption Bands ◽  
Considerable Evidence ◽  
Different Seasons ◽  
Insight Into ◽  
Accurate Indicator

Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.

Rapid in-season detection of herbicide resistant Alopecurus myosuroides using a mobile fluorescence imaging sensor

2016 ◽  
Vol 89 ◽  
pp. 170-177 ◽  
Author(s):  
Pei Wang ◽  
Gerassimos Peteinatos ◽  
Hui Li ◽  
Roland Gerhards
Keyword(s):  
Fluorescence Imaging ◽  
Herbicide Resistant ◽  
Alopecurus Myosuroides ◽  
Imaging Sensor

Source Camera Identification Based on Sensor Readout Noise

2010 ◽  
Vol 2 (3) ◽  
pp. 28-42 ◽  
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

scholarly journals Stress Detection from Multimodal Wearable Sensor Data

2020 ◽  
Vol 771 ◽  
pp. 012028
Author(s):  
Fitri Indra Indikawati ◽  
Sri Winiarti
Keyword(s):  
Sensor Data ◽  
Wearable Sensor ◽  
Stress Detection

scholarly journals Modification of a gas exchange system to measure active and passive chlorophyll fluorescence simultaneously under field conditions

AoB Plants ◽  
2020 ◽  
Author(s):  
Eliot W Meeker ◽  
Troy S Magney ◽  
Nicolas Bambach ◽  
Mina Momayyezi ◽  
Andrew J McElrone
Keyword(s):  
Chlorophyll Fluorescence ◽  
Steady State ◽  
Gas Exchange ◽  
Near Infrared ◽  
Pulse Amplitude ◽  
Bottom Plate ◽  
Pam Fluorometer ◽  
Promising Tool ◽  
Leaf Level ◽  
Gas Exchange System

Abstract Solar-induced fluorescence (SIF) is a promising tool to estimate photosynthesis across scales; however, there has been limited research done at the leaf-level to investigate the relationship between SIF and photosynthesis. To help bridge this gap, a LI-COR LI-6800 gas exchange instrument was modified with a visible-near-infrared (VIS-NIR) spectrometer to measure active and passive fluorescence simultaneously. The system was adapted by drilling a hole into the bottom plate of the leaf chamber and inserting a fiber-optic to measure passive steady-state fluorescence (Ft,λ, analogous to SIF) from the abaxial surface of a leaf. This new modification can concurrently measure gas exchange, passive fluorescence, and active fluorescence over the same leaf area and will allow researchers to measure leaf-level Ft,λ in the field to validate tower-based and satellite measurements. To test the modified instrument, measurements were performed on leaves of well-watered and water stressed walnut plants at three light-levels and a constant air temperature. Measurements on these same plants were also conducted using a similarly modified Walz GFS-3000 gas exchange instrument to compare results. We found a positive linear correlation between Ft,λ measurements from the modified LI-6800 and GFS-3000 instruments. We also report a positive linear relationship between Ft,λ and normalized steady-state chlorophyll fluorescence (Ft/Fo) from the pulse-amplitude modulation (PAM) fluorometer of the LI-6800 system. Accordingly, this modification will inform the link between spectrally resolved Ft,λ and gas-exchange – leading to improved interpretation of how remotely sensed SIF tracks changes in the light reactions of photosynthesis.

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

IoT Based Stress Detection for Organic Lettuce Farms Using Chlorophyll Fluorescence (ChF)

2018 ◽  
Author(s):  
Supachai Puengsungwan ◽  
Kamon Jiraserccamomkul
Keyword(s):  
Chlorophyll Fluorescence ◽  
Stress Detection
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