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15 - 19 April 2018
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Photonics research for agriculture, food safety & water quality

See what agricultural applications you can find at SPIE Defense + Commercial Sensing

Explore agricultural applications of sensing, imaging, and photonics technologies at SPIE Defense + Commercial Sensing, including UAVs, hyperspectral imaging, phenotyping, infrared thermography, and more.

Our topical tracks help you quickly locate potential items of interest in the 2018 Defense + Commercial Sensing program, such as sessions, papers, vendors, and courses. Explore the information below to see what may interest you. 

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Registration opens late December

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Browse applicable conference papers

Below are conferences and papers that include significant technical content related to agricultural applications. SPIE Defense + Commercial Sensing 2018 includes 50 conferences and 1,900 papers and many of them may be of interest to those interested in agricultural applications, however these 6 conferences and 28 papers have been identified as containing specific content that may be of particular interest. 

Review these 6 conferences

 • Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
 • Sensing for Agriculture and Food Quality and Safety
 • Next-Generation Spectroscopic Technologies
 • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
 • Thermosense: Thermal Infrared Applications
 • Energy Harvesting and Storage: Materials, Devices, and Applications

Review the 28 papers below
These papers are listed by conference and paper number

Applications of hyperspectral image analysis for precision agriculture
Paper 10639-42

Author(s):  Stan Martin, Bayer CropScience LP (United States), et al.
Conference 10639: Micro- and Nanotechnology Sensors, Systems, and Applications X
Session 8: Remote Sensing Techniques and Applications

With world projections of global population running to 9.5-10 billion by mid century, it has becoming apparent that increasing food production will soon become an existential problem. However, the world has been here before. In the mid 1950’s parts of the developing world, especially in Mexico and India were facing an existential food crisis. This situation helped spark a series of innovations that collectively became known as the “Green Revolution”. Recent advances in sensor technology, computer processing power, and algorithmic development have ushered in a new era of precision farming. It is now possible to obtain precise measurements of biological phenomena on very large scales. This development is part of a continuum of advances that have ushered in “Green Revolution 2.0”. Digital Farming technologies are the tip of the spear in this new era. In this paper we review the timeline and development of both biological and sensor technologies that are just now beginning to converge.


Enhanced pedestrian safety awareness at crosswalks via networked lidar, thermal imaging, and sensors
Paper 10643-13

Author(s):  Zachary A. Weingarten, Florida Polytechnic Univ. (United States), et al.
Conference 10643: Autonomous Systems: Sensors, Vehicles, Security and the Internet of Everything
Session 2: Object Sensing for Detection, Classification, and Autonomous Operations

The system makes use of thermal imaging, LiDAR, conventional imagers, and sensors to distinguish between cars, people, animals, and other objects that may interact with a crosswalk at or near an intersection. A mesh network of these systems as nodes enables the coordination of information, alerts and/or interfaces to coordinate control of the lights as well as alert vehicles and people crossing at a crosswalk or an intersection. The data could also be used to enhance coordination of IoT or mobile devices such as those integrated with autonomous vehicles and the Intelligent Transportation System infrastructure to predict how to handle a pedestrian interaction with crosswalks or intersection. The goal of the system is to enhance pedestrian safety at crosswalks or intersections via LiDAR, thermal imaging, conventional imagers, shared interfaces, networks and other resources.


Necessary steps for the systematic calibration of a multispectral imaging system to achieve a targetless workflow in reflectance estimation: a study of Parrot SEQUOIA for precision agriculture
Paper 10644-42

Author(s):  Clément Fallet, Parrot S.A. (France), et al.
Conference 10644: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Session 9: Applications

Comparison of remote sensing data from different weather conditions, time of day and geographic locations requires absolute reflectance. Reflectance estimation for precision agriculture demands detailed knowledge of imaging and ambient irradiance sensors. This work presents the difficulties and conditions for sensor characterization and modelling; and shows how these apply to automated calibration at an industrial scale using an integrating sphere and a reference spectrophotometer. Industry standard imaging of reflectance targets completes the system calibration. The consequences of the difference between calibration test bench geometry and the real world are discussed opening the road to a targetless workflow for multispectral cameras.


Design-optimization and performances of multispectral (VIS-SWIR) photodetector and its array
Paper 10656-21

Author(s):  Jaydeep Dutta, Banpil Photonics, Inc. (United States), et al.
Conference 10656: Image Sensing Technologies: Materials, Devices, Systems, and Applications V
Session 5: Advanced Photodetectors and Focal Plane Array (FPA)

A novel broadband (VIS-SWIR) photodetector is developed for focal plane array (FPA) for military, security, and industrial imaging applications. The photodetector is based on InGaAs and fabricated on InP substrate, exhibiting high sensitivity, high quantum efficiency, and yet cost-effective. In order to realize a small weight, power, and cost effectiveness (SWAP-C) camera, the photodetector must have low dark current at high operating temperatures, which saves power for cooling. This paper will explain photodetector structure, design-simulation for optimizing the parameters, and performance of the photodetector and its array. We investigate the device structure and the theory of the photodetector. Electrical and optical characteristics of the photodetectors will be also presented in this paper.


Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management
Paper 10664-1

Author(s):  Mac McKee, Utah State Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs

Small, unmanned aerial systems (sUAS) are used as remote sensing devices for agriculture with growing frequency. These systems place limitations on the types and quality of the cameras that can be flown. This, in turn, limits the quality of the information that can be generated for the grower. This paper statistically examines issues of how errors in sensor spectral response, orthorectification accuracy, and spatial resolution can affect the estimation of information products of potential interest to growers, such as plant nutrition and precision fertilization. The paper relies on high-resolution data collected in 2016 over a commercial vineyard located near Lodi, California.


Quality assessment of radiometric calibration of UAV image mosaics
Paper 10664-3

Author(s):  Cody Bagnall, Texas A&M Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs

UAV (unmanned aerial vehicle) based imaging produces vast amounts of data that could be used to improve the efficiency of agricultural inputs. One reason this ability has not yet been realized is that producing radiometrically calibrated UAV image mosaics is difficult. This paper presents an investigation of a field-based image-mosaic calibration procedure. A commercial off-the-shelf fixed-wing small UAV and a five-band multispectral sensor were used with multiple exposure settings. We evaluate the quality of the radiometric calibration procedure for UAV image mosaics by comparing them to high quality calibrated manned aircraft and satellite images collected on the same day at roughly the same time.


Correction of in-flight luminosity variations in multispectral UAS images, using a luminosity sensor and camera pair for improved biomass estimation in precision agriculture
Paper 10664-4

Author(s):  Jean-Marc Gilliot, AgroParisTech (France), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 1: Collecting Reliable Image Data with UAVs

This work studies the ability to correct luminosity variations on images from UAS flights under varying weather conditions. The Parrot SEQUOIA multispectral camera paired with its Sunshine luminosity sensor acquired data correlated with a field spectroradiometer on reference reflectance targets. Finally two different types of UAS carried out several sets of flights. All data were analyzed with and without the Sunshine sensor correction to quantify its improvement to the quality of reflectance measurements and biomass estimates.


Detection of pea flowering using proximal and aerial remote sensing
Paper 10664-8

Author(s):  Chongyuan Zhang, Washington State Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 2: Proximal and Remote Sensing for Phenotyping

In plant breeding, the time and length of flowering is an important phenotype that determines the seed yield potential in plants. Currently, flowering traits are visually assessed, which can be time-consuming and subjective. In this study, high-throughput phenotyping (HTP) techniques, including proximal sensing using a customized phenomic cart and remote sensing with an unmanned aerial system, were applied to monitor the dry pea flowering in an advanced variety trial. Visible and near-infrared digital images acquired were processed to determine the flowering period and intensity. Preliminary data analysis showed better feasibility and accuracy in detecting flowers using proximal sensing techniques and low-altitude remote sensing techniques. High-throughput phenotyping techniques will potentially improve the throughput and objectivity of detecting flowering in pea and other flowering crops, and contribute to development of new cultivars in breeding programs.


Phenotyping of sorghum panicles using unmanned aerial system (UAS) data
Paper 10664-9

Author(s):  Anjin Chang, Texas A&M Univ. Corpus Christi (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 2: Proximal and Remote Sensing for Phenotyping

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days for precision agriculture and High Throughput Phenotyping (HTP). Attributes of sorghum panicle, especially, are critical information to assess overall crop condition, irrigation, and yield estimation. In this study, it is proposed a method to extract phenotypes of sorghum panicles using UAS data. UAS data were acquired with 85% overlap at an altitude of 10m above ground to generate super high resolution data. Orthomosaic, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the imagery from UAS. Sorghum panicles were identified from orthomosaic and DSM by using color ratio and circle fitting. The cylinder fitting method and disk tacking method were proposed to estimate panicle volume. Yield prediction models were generated between field-measured yield data and UAS-measured attributes of sorghum panicles.


Inter-comparison of thermal measurements using ground-based sensors, airborne thermal cameras, and eddy covariance radiometers
Paper 10664-12

Author(s):  Alfonso F. Torres-Rua, Utah State Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 3: Thermal and Hyperspectral Imaging from UAVs

The increasing availability of on-ground sensors and UAV-borne thermal cameras, along with eddy covariance radiometers, for estimation of agricultural parameters such as Evapotranspiration, implicitly rely on the assumption that information produced by these sensors is interchangeable or compatible. This work presents a comparison between on-ground infrared radiometer (IRT), microbolometer thermal cameras used in UAVs and thermal radiometers used in eddy covariance towers. as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program).


A low-cost method for collecting hyperspectral measurements from a small unmanned aircraft system
Paper 10664-15

Author(s):  Ali Hamidisepehr, Univ. of Kentucky (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 3: Thermal and Hyperspectral Imaging from UAVs

This study aimed to develop a spectral measurement platform for deployment on a sUAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using an embedded computer programmed to interface with the sUAS autopilot for autonomous data acquisition. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system.


Disease detection and mitigation in a cotton crop with UAV remote sensing
Paper 10664-19

Author(s):  J. Alex Thomasson, Texas A&M Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 4: Detecting Yield, Disease, and Water Stress from UAVs

A disease called cotton root rot (CRR) can devastate cotton crops unless a specific fungicide is applied at planting to protect the roots of the plants. Remote sensing has proven effective at identifying locations of the disease, and fungicide application can be limited to the areas where the fungus poses a problem. Since UAVs can efficiently provide highly detailed images of crops, image data of a field with CRR problems were collected with a UAV in 2015 and used to direct fungicide application in 2017. The result was that fungicide application was minimized without significantly reducing protection of the plants.


Experimental approach to detect water stress in ornamental plants using UAV-imagery
Paper 10664-20

Author(s):  Ana de Castro, Instituto de Agricultura Sostenible (Spain), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 4: Detecting Yield, Disease, and Water Stress from UAVs

Accurate, reliable and timely crop water status measurements could improve irrigation efficiency and optimize water use in agriculture. Containerized ornamental crops provide a unique opportunity to apply UAV platform due to relatively small area of production, a diversity of plant species, and unbuffered growing media requiring continual inputs of water; making UAV a timely alternative to on-ground data collection. This research evaluated the potential of UAV-based images to estimate crop water status of multiple taxa. An algorithm based on the object-based image analysis (OBIA) paradigm was developed to accurately identify water stressed and non-stressed plants.


Evaluation of multispectral unmanned aerial systems for irrigation management
Paper 10664-23

Author(s):  José L. Chávez, Colorado State Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 5: Analytics for UAV-based Crop Management

Growing competition for water is incentivizing the implementation of deficit irrigation. Thus, there is a need to accurately map actual crop evapotranspiration (ETa) to more efficiently manage and document irrigation. An alternative is the use of remote sensing (RS) platforms. Unmanned Aerial Systems (UAS) can fly frequently and acquire very high spatial resolution images. Multispectral UASs (fixed-wing and multi-rotor) flew over irrigated corn fields, in northern Colorado, to evaluate the capabilities of the RS systems on irrigation management. Soil water content sensors were used in the evaluation. This study discusses the benefits of multispectral UAS platforms in irrigation management.


Using UAVs to improve nitrogen management of winter wheat
Paper 10664-24

Author(s):  Joseph Oakes, Virginia Polytechnic Institute and State Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 5: Analytics for UAV-based Crop Management

Nitrogen (N) timing and application rate are challenging decisions for wheat growers. Optimum yields require high tiller density and adequate N content, but over fertilization causes lodging with adverse effects on yield and quality. The advent of the unmanned aerial vehicle (UAV) and new remote sensing technologies creates the opportunity to determine crop N need cheaper and faster. Several sensors have the ability to determine the crop’s nutrition status and the need for fertilizer. If successful, this technology could enable growers to quickly make N fertilizer decisions.


UAV videos to extend research to producers
Paper 10664-25

Author(s):  Louis Wasson, Geosystems Research Institute (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 5: Analytics for UAV-based Crop Management

UAV’s are used to take short movies of ongoing corn research projects. Using Mississippi State University editing professionals the movies are narrated to describe the research in what we are doing and why. The view from the UAV above the plots offers a unique perspective on what is going in our fields. At a Field Day in 2016 the video was a spectacular success showing both the producers/scientists and the research plots. We will use our agricultual research, UAV and video editing expertise to create future educational short movies that will be useful for the agricultural boards, producers, Mississippi State University and perhaps even for use in the classroom to demonstrate clear visuals of our latest agricultural research projects.


Evaluating UAVs under a multi-platform system in modeling crop characteristics
Paper 10664-27

Author(s):  Gregory Rouze, Texas A&M Univ. (United States), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 6: Innovative UAV Applications


Evaluating the capabilities of Sentinel-2 and Tetracam RGB+3 formulti-temporal detection of thrips on capsicum
Paper 10664-28

Author(s):  Jayantrao D. Mohite, Tata Consultancy Services Ltd. (India), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session 6: Innovative UAV Applications

Thrips is serious pest in Capsicum which goes undetected in initial phase so its timely detection is important. In this paper, we address the problem of detection of low infestation thrips on Capsicum using hyperspectral remote sensing data simulated to Sentinel-2 and Tetracam RGB+3 bands. The data is collected from 213 bands with wavelength ranging from 350 nm to 1052 nm over a period of 1 month using handheld spectroradiometer. We evaluated the performance of tuned random forest classifier by feeding different set of features such as full feature set of 213 bands, feature set selected by LASSO, feature set simulated to Sentinel-2 bands and Tetracam RGB+3. Classification accuracy of 92.81, 90.3, 85.13 and 87.45% was achieved when considering 213 bands, features selected by LASSO, Sentinel-2 like band simulations and Tetracam like simulations respectively.


Using hyperspectral sensors for crop vegetation status monitoring in precision agriculture
Paper 10664-32

Author(s):  Marius Cristian Luculescu, Transilvania Univ. of Brasov (Romania), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session PTue: Poster Session

The world is changing. Day by day we are facing with more and more changes regarding the climate, technology, economy and society. All of these place their mark on agro ecosystems. Major economic and environmental impacts can be obtained by providing water and nutritional supplement just to those plants that need them, only when they need and in proper quantities. In order to do this, a real time management of agricultural crops is necessary. The paper presents a solution for crop vegetation status monitoring in precision agriculture, based on hyperspectral sensors, namely on spectrometers, placed on an UAV (Unmanned Aerial Vehicle).


MoniSCAN: Software for multispectral monitoring of the crops vegetation status
Paper 10664-33

Author(s):  Marius Cristian Luculescu, Transilvania Univ. of Brasov (Romania), et al.
Conference 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
Session PTue: Poster Session

An efficient crops management, in the continuous changes context especially regarding climate, requires real time monitoring of soil resources and vegetation dynamics. A part of this process is the precision agriculture that supposes to investigate crops so that to allocate inputs as water and fertilizer for example, only to the plants that need them, at the proper moment and in proper quantities. For monitoring the crops vegetation status different solutions are available on the market. Most of them acquire spectral data, process and represent them on maps offering support for proper farmer decision. MoniSCAN is such a software developed in a research project.


Study of visible imaging and near-infrared imaging spectroscopy for plant root phenotyping
Paper 10665-1

Author(s):  Thomas Arnold, CTR Carinthian Tech Research AG (Austria), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 1: Hyperspectral and Multispectral Imaging for Foods

In modern agriculture drought is a major cause of low yields worldwide. Therefore, imaging systems that enable to study the interactions between plant and soil are a way towards better understanding of crop water supply. In this paper the combination of visible (VIS) imaging and near-infrared (NIR) imaging spectroscopy for plant root phenotyping is presented. The system provides increased image contrast which allows for a more reliable segmentation of the roots from the soil and additional information to be extracted. Moreover, it is possible to visualize the water distribution in the soil in close proximity to the roots.


Continuous gradient temperature Raman spectroscopy of unsaturated fatty acids: applications for fish lipids and rendered meat source identification
Paper 10665-3

Author(s):  C. Leigh Broadhurst, Agricultural Research Service (United States), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 1: Hyperspectral and Multispectral Imaging for Foods

Continuous gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry to Raman spectroscopy. 20 Mb three-dimensional data arrays with 0.2°C increments and first/second derivatives allow complete vibrational assignments. We applied GTRS to eight unsaturated fatty acids with one double bond to six, and two phosphatidyl cholines, resulting in new 3D structures and insight into why diets high in fish are healthy. The highly improved lipid spectroscopy was also applied to differentiate pork and chicken meat and bone meal supplied from commercial rendering. Twenty percent pork mixed into chicken meal can be identified rapidly.


MCT-based shortwave infrared hyperspectral imaging system for the detection and quantification of adulterants in powder samples
Paper 10665-8

Author(s):  Hoonsoo Lee, Agricultural Research Service (United States), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 2: Sensing for Food Quality and Safety I


Non-targeted and targeted Raman imaging detection of chemical contaminants in food powders
Paper 10665-14

Author(s):  Jianwei Qin, Agricultural Research Service (United States), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 4: High Throughput Inspection

Economically motivated adulteration and fraud for food powders are emerging food safety risks that threaten the health of the general public. This study developed non-targeted and targeted methods to detect food powder adulterants based on Raman chemical imaging technique. Line-scan hyperspectral Raman images were acquired using a 785 nm line laser from selected powdered foods and ingredients mixed with representative adulterants and illegal additives. Raman data analysis algorithms were developed to fulfill non-targeted and targeted contaminant detection. For both methods, chemical images were created to map the contaminant particles mixed in the food powders.


Miniature near infrared spectroscopy spectrometer and information and communication technologies to guarantee the integrity of the EU high added-value acorn Iberian pig ham
Paper 10665-20

Author(s):  Ana Garrido-Varo, Univ. de Córdoba (Spain), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 5: Visible and Near Infrared Imaging For Foods

This research is framed within FoodIntegrity, EU sponsored project(7th FP). The main goal of the research to be done is to provide industrials, producers and consumers with a methodology based in low-cost, portable and miniature NIRS sensors and information and communication technologies for process control and voluntary labelling, to guarantee the integrity of the EU high added-value as the “acorn Iberian pig ham”. The present study is focussed in transferring a database (470 samples) of IP tissue - analysed in a FOSS-NIRSystems 6500 (FNS6500) spectrometer, during the seasons 2009-2011 - to a portable/miniature instrument MicroNIR-Onsite, VIAVI (MN1700)


A RAW-imaging technique that deploys GIS-based parameters for nutrient analysis of leaves
Paper 10665-21

Author(s):  Ekdeep Singh Lubana, Indian Institute of Technology Roorkee (India), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session 5: Visible and Near Infrared Imaging For Foods

Since long, efforts have been made for using Handheld cameras for quantization of leaf-nutrient content. Our research reviews these efforts, figures the shortcomings, and proposes a highly robust and accurate methodology for quantification of leaf-nutrient content, via RAW-imaging cameras. An apparatus has been specifically designed for the purpose. The standard GIS spectral parameters, such as NDVI, are modified to account for camera’s spectral response and the spectral bandwidth of the incident energy - for which optimized light sources have been deployed. The wavelengths have been decided for by calculating their sensitivity to leaf-chlorophyll content. Under the precisely designed apparatus, a pilot implementation has been conducted for calculation of leaf-nitrogen content. The results, along with the processed images, are showcased.


Isolation of highly selective phage-displayed oligopeptide probes for detection of listeria monocytogenes in samples containing clorox and chlorine dioxide
Paper 10665-22

Author(s):  I-Hsuan Chen, Auburn Univ. (United States), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session

Listeria monocytogenes is the major etiologic agent for foodborne Listeriosis in humans from consumption of ready-to-eat (RTE) food. According to FDA’s Bacterial Analysis Manual, L. monocytogenes in RTE food is detected via microbiological culture-based tests, qPCR, and pulsed-field gel electrophoresis. Those methods are time consuming and require dedicated laboratory facility. Thus, to develop a real-time L. monocytogenes biosensor, we isolated L. monocytogenes specific oligopeptides displayed on bacteriophages using modified biopanning procedures. In order to account for major temperature dependent morphological alterations of L. monocytogenes at 4°C versus 37°C, we used bacterial cells adapted to either temperature as the ligand in our biopanning. Those isolated probes can be used on our magnetoelastic biosensor platforms for real-time detection of L. monocytogenes in RTE foods stored at 4C or in human samples/fluids for bacterium adapted to 37°C.


Applications of convolutional neural networks (CNN) for food quality and safety using hyperspectral imaging
Paper 10665-25

Author(s):  Hoonsoo Lee, Agricultural Research Service (United States), et al.
Conference 10665: Sensing for Agriculture and Food Quality and Safety X
Session PTue: Poster Session


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