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Conference 11754

Sensing for Agriculture and Food Quality and Safety XIII

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  • All-Symposium Plenary Session I
  • All-Symposium Plenary Session II
  • All-Symposium Plenary Session III
  • Next Generation Sensor Systems and Applications Track Plenary Session
  • NIR Spectroscopy
  • Raman Spectroscopy and Imaging
  • Hyperspectral Imaging
  • UAV-based Imaging
  • Biosensing
  • Poster Session
  • Front Matter: Volume 11754
Session LIVE: All-Symposium Plenary Session I
11741-402
Author(s): Timothy P. Grayson, Defense Advanced Research Projects Agency (United States)
On-demand | Presented Live 12 April 2021
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Defense Advanced Research Projects Agency (DARPA) is developing the technologies to conduct Mosaic Warfare. These are the tools and infrastructure to enable dynamic composition and operation of adaptive, disaggregated systems of systems architectures. When applied to sensing, the tools of Mosaic enable sensing to be conducted as a “team sport” in which we can move away from expensive, complex, exquisite, multi-function monolithic sensors to highly distributed, hyper-specialized sensors in which each individual sensor addresses only a small part of an overall function. This specialization enables deployment of sensors in greater numbers and smaller, cheaper platforms. The presentation will discuss how DARPA is implementing Mosaic, the implications for sensing, and potential dual-use applications in the commercial sector.
Session LIVE: All-Symposium Plenary Session II
11741-403
Author(s): Donald A. Reago, CCDC C5ISR (United States)
On-demand | Presented Live 12 April 2021
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This talk will examine the functions of sensing (and sensors) in modern warfare in relation to increasing complexities regarding asymmetric threats, multi-domain operations, and command and control layers and their demands on information processing, situational awareness and networking. New sensors and SWAP-C improvements to existing sensors are increasing numbers and availability of collectors that produce more and more data, while, simultaneously, demand/consumption is also increasing with more users wanting more and more situational awareness. New bottlenecks become evident and drive new requirements needing new solutions for increased data processing, automation and intelligent processing.
Session LIVE: All-Symposium Plenary Session III
11741-401
Author(s): Rita C. Flaherty, Lockheed Martin Corp. (United States)
On-demand | Presented Live 13 April 2021
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Pandemic and budget related impacts to the global supply chain have driven a change in the way we approach partnerships with small businesses and the manpower they provide. This presentation will focus on the importance of cultivating productive and mutually beneficial relationships with suppliers while simultaneously driving economic development in local communities. The talk will also cover the challenge of virtual recruiting, encouraging diversity in the workforce while attracting local talent and the avenues for small business to connect with Lockheed Martin.
11741-400
Author(s): Jean-Charles Lede, Air Force Research Lab. (United States)
On-demand | Presented Live 13 April 2021
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Autonomy and AI have made tremendous progress in recent years to the point where operational applications of these technologies can provide decisive advantages. This presentation will discuss the approach recommended to rapidly field autonomy and AI capabilities at scale including the development of a common platform, addressing trust issues, and agile methodology. Examples in sensor exploitation and business processes will be used to demonstrate the operational value of current generation of AI. However, this generation has limitations, and the talk will conclude with future research required to expand the safe, ethical, and effective use of these technologies.
Session LIVE: Next Generation Sensor Systems and Applications Track Plenary Session
11746-300
Author(s): Stuart H. Young, U.S. Army Combat Capabilities Development Command (United States)
On-demand | Presented Live 14 April 2021
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The self-driving car industry has made great autonomy advances, but mostly for well-structured and highly predictable environments. In complex militarily-relevant settings, robotic vehicles have not demonstrated operationally relevant speed and aren’t autonomously reliable. While vehicle platforms that can handle difficult terrain exist, their autonomy algorithms and software often can’t process and respond to changing situations well enough to maintain necessary speeds and keep up with soldiers on a mission. DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program aims to make sure algorithms aren’t the limiting part of the system and that autonomous combat vehicles can meet or exceed soldier driving abilities. RACER will demonstrate game-changing autonomous UGV mobility, focused on speed and resiliency, using a combination of simulation and advanced platforms. It tests algorithms in the field at DARPA-hosted experiments across the country on a variety of terrain. The program provides UGV platforms that research teams can use to develop autonomous software capabilities through repeated cycles of tests on unstructured off-road landscapes. Goals include not only autonomy algorithms, but also creation of simulation-based approaches and environments that will support rapid advancement of self-driving capabilities for future UGVs.
NIR Spectroscopy
11754-1
Author(s): Dolores C. Pérez-Marín, Irina Torres, Miguel Vega-Castellote, Ana Garrido-Varo, María-Teresa Sánchez, Univ. de Córdoba (Spain)
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Near Infrared (NIR) Spectroscopy is a powerful technology which can be implemented as a non-destructive tool to make decisions related to cultural practices and harvesting. However, prior to the incorporation of NIR sensors at field level as an analytical technique, a routine analysis procedure should be established. In this sense, this research is focused on the development of a methodology based on the use of a portable NIR instrument to monitor the growth process and to establish the optimum harvest time of spinach plants in the field.
11754-2
Author(s): Miguel Vega-Castellote, María-Teresa Sánchez, Irina Torres, Ana Garrido-Varo, Dolores C. Pérez-Marín, Univ. de Córdoba (Spain)
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Near infrared (NIR) spectroscopy can be a fast and reliable candidate for the non-destructive and in-situ classification of almonds by bitterness, when analysed in bulk. With that purpose, in-shell and shelled sweet and bitter almonds were analysed using a handheld diode array NIR spectrophotometer (950-1650 nm). Models were constructed using partial least squares-discriminant analysis (PLS-DA) and the optimum threshold value was set up using the Receiver Operating Characteristic (ROC) curves. The models correctly classified 95 % of in-shell and 100 % of shelled samples belonging to the external validation sets.
11754-3
Author(s): Dolores C. Pérez-Marín, Univ. de Córdoba (Spain); Begoña De la Roza, SERIDA (Spain); José A. Entrenas, María del Mar Garrido-Cuevas, Ana Garrido, Univ. de Córdoba (Spain)
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Feeding dairy cows with Total Mixed Rations (TMR) is a cost-effective way to obtain high milk yield. Animal nutritionists are demanding accurate information on the main chemical constituents of TMR to properly feed lactating cows. The use of portable NIRS devices could provide an affordable answer. This work analysed a total of 121 TMR using two portable NIRS instruments for the prediction of dry matter, crude protein and neutral detergent fibre. The paper evaluated whether there were significant differences between the predictive capacities of the models developed from analytical data expressed “ as dry matter” or “ as is basis”.
11754-4
Author(s): Fayas Asharindavida, Omar Nibouche, James Uhomoibhi, Hui Wang, Jordan Vincent, Univ. of Ulster (United Kingdom)
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Food authentication and quality checks can be carried out by applying machine learning algorithms on spectral data acquired from miniature spectrometers. This is a very appealing solution as the cost-effectiveness of miniature spectrometers extends the range of consumer electronics available for ordinary citizens in the fight against food fraud, widens the range of their applications and shortens the processing time for any in-situ scenario. The aim of this work is to gauge the ability of such a device to differentiate between pure olive oil from ones adulterated with vegetable oils on a relatively large dataset. The paper presents a pipeline encompassing various steps including data pre-processing, dimension reduction, classification and regression analysis. An important consequence of this work is that cost-effective miniature spectrometers augmented with a suitable machine learning component can attain comparable results obtained using non-portable and more expensive spectrometers
Raman Spectroscopy and Imaging
11754-7
Author(s): Feifei Tao, Mississippi State Univ (United States); Haibo Yao, Zuzana Hruska, Russell Kincaid, Mississippi State Univ. (United States); Kanniah Rajasekaran, Agricultural Research Service (United States)
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Hyperspectral Imaging
11754-9
Author(s): Alexia Naudé, Paul J. Williams, Stellenbosch Univ. (South Africa)
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Wheat (Triticum spp.) is a widely grown cereal crop and is one of the most important staple foods internationally. Grading is an imperative step in wheat production to ensure that the grains are of acceptable quality and safety for food processing and consumption. Current wheat grading practices are performed manually which is tedious, time-consuming, and subjective. This study aimed to investigate the feasibility of NIR hyperspectral imaging (HSI) and chemometrics to discriminate sound wheat from four common defects. Defective wheat included heat-damaged, Fusarium-damaged, sprout-damaged, and immature kernels. A wide variety of pre-processing techniques and classification algorithms (logistic regression, partial least squares-discriminant analysis, linear discriminant analysis, k-nearest neighbours, decision trees, random forests and support vector machines) were evaluated for both two-way and multiclass analyses. For the two-way classifications, accuracies between 98.6% and 100% were achieved for each defective category. The multiclass analyses had a decreased performance, where the best model attained an accuracy of 84.6%. Given that agricultural products vary significantly in inherent characteristics due to genetics, cultivation practices, handling and storage, the overall results achieved are highly successful. This study shows that HSI is capable of effectively discriminating sound wheat from common occurring defects, offering a promising alternative grading technique to the cereal industry.
11754-10
Author(s): Irina Torres, Dolores C. Pérez-Marín, Miguel Vega-Castellote, Ana Garrido-Varo, María-Teresa Sánchez, Univ. de Córdoba (Spain)
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The determination of the fatty acid profile in almonds has a huge interest to establish their nutritional value. The objective of this study was the determination of the two main unsaturated fatty acids -oleic and linoleic, in shelled almonds analysed in bulk using a HSI system working in the spectral range 946.6 to 1648.0 nm. The predictive models were developed using the mean spectrum extracted from the ROI of each sample and PLS regression. The results confirm the feasibility of HSI as a non-destructive analytical tool to assess the lipid composition and its distribution in the almonds processed in bulk.
UAV-based Imaging
11754-14
Author(s): Billie Morgan, Agricultural Research Service (United States); Matthew D. Stocker, Oak Ridge Institute for Science and Education (United States); Yakov Pachepsky, Moon S. Kim, Agricultural Research Service (United States)
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Microbial water quality monitoring is an essential component of food safety. E.coli bacterium is the major indicator organism used in assessing microbial water quality but dense sampling of water to assess spatial variability is impractical. The objective of this work was to test the hypothesis that sUAS imaging can provide information about the differences in E. coli habitats across irrigation ponds and guide water sampling. The random forest machine learning algorithm was applied to relate ground sampling data with image sections. Overall, a reasonable estimation of E.coli concentrations from imagery data is possible and benefits from tuning algorithm control parameters.
11754-15
Author(s): Samir Castineira, Tamim Delwar, Rodolfo Duran, Nezih Pala, Florida International Univ. (United States)
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Unmanned Aerial Vehicle (UAV) Agricultural Data Acquisition System, a drone aided system designed to achieve precision farming with low cost and high accuracy. It is composed of two subunits, the Soil Data Monitoring Probe (SDMP) and Airborne Data Acquisition System (ADAS). The SDMP, is a self-sufficient unit when deployed, by itself or in a grid configuration, gathers soil metrics, and stores them for later wireless data collection. The ADAS, a GPS enabled drone attachment, which collects the stored SDMP data as the drone comes in proximity to an SDMP unit, using geolocation.
Biosensing
11754-18
Author(s): Hyun Jung Min, Euiwon Bae, Purdue Univ. (United States)
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Poster Session
11754-22
Author(s): Qinghui Guo, Yankun Peng, Yongyu Li, China Agricultural Univ. (China); Kuanglin Chao, Agricultural Research Service (United States); Feifei Tao, Mississippi State Univ. (United States)
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In this study, a method for subsurface food inspection was presented based on a newly developed line-scan spatially scattering Raman spectroscopy technique. A spatially offset Raman spectroscopy system based on line-scan Raman chemical imaging system was built, which was used to collect spatially offset Raman spectra from the samples. Under the condition of one CCD exposure, the spatially offset Raman spectroscopy system can collect a series of Raman spectra simultaneously in a narrow space interval and a wide offset range. Through data analysis, the rare clenbuterol species in the sample can be determined by the position of characteristic peaks. The system was used to collect salbutamol Raman signal, and the results showed that the characteristic peaks of salbutamol were consistent with the standard characteristic peaks, so it was possible to use the system to detect lean meat in meat.
11754-24
Author(s): Giuseppe Bonifazi, Giuseppe Capobianco, Riccardo Gasbarrone, Silvia Serranti, Sapienza Univ. di Roma (Italy)
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The utilization of hyperspectral imaging (HSI) in the short-wave infrared region (SWIR: 1000-2500 nm) was applied for hazelnuts quality control. Hyperspectral images of different hazelnuts and contaminant samples were acquired by a pushbroom hyperspectral device. Classification models, based on Partial Least Squares Discriminant Analysis (PLSDA) combined with variable selection methods, were built to identify 3 classes of products: edible hazelnuts, shells and inedible hazelnuts. Classification results suggest that this methodological approach could increase processing speed of the recognition, compared to full spectrum mode, making possible online application directly at plant site.
Front Matter: Volume 11754
Conference Chair
USDA Agricultural Research Service (United States)
Conference Chair
Chungnam National Univ. (Korea, Republic of)
Program Committee
USDA Agricultural Research Service (United States)
Program Committee
USDA Agricultural Research Service (United States)
Program Committee
Univ. de Córdoba (Spain)
Program Committee
Korea Research Institute of Standards and Science (Korea, Republic of)
Program Committee
Naoshi Kondo
Kyoto Univ. Graduate School of Agriculture (Japan)
Program Committee
USDA Agricultural Research Service (United States)
Program Committee
USDA Agricultural Research Service (United States)
Program Committee
USDA Agricultural Research Service (United States)
Program Committee
China Agricultural Univ. (China)
Program Committee
Dolores Pérez-Marín
Univ. de Córdoba (Spain)
Program Committee
Amrita Sahu
Altria Group, Inc. (United States)
Program Committee
Stellenbosch Univ. (South Africa)
Program Committee
Mississippi State Univ. (United States)
Program Committee
Zhejiang Univ. (China)
Program Committee
USDA Agricultural Research Service (United States)