Dr. Panigrahi and his research group have contributed significantly in the area of sensors, sensing systems, computer-based intelligent systems as well as decision support systems including artificial intelligent techniques. His group is one of the early pioneer in developing such sensors and sensing systems from 1990s.
In mid-1990’s, Dr. Panigrahi created new field-scale non-destructive sensors to determine the quality of selected agricultural products (protein content of wheat, sugar content of sugar beets) during harvesting. This work resulted in three approved (U.S.A.) patents for Dr. Panigrahi (as the lead inventor) and his colleagues. The developed methods can be modified for sensing other quality parameters. These patents, first of its kind, were more than 15 years ahead of its time. Today, the developed technologies are a part of the sought-after solutions for the digital agriculture revolution.
In early 1990’s, Dr. Panigrahi conceived the idea and worked with a plant pathologist to develop an artificial neural network (machine learning) model for predicting plant diseases in wheat.This work was an enabling technology for crop disease management and its impact was very high for the researchers and the growers. In precision farming domain, his contributions were also in: a) non-contact optical sensor development to predict nitrate and chlorophyll content of plant leaves and b) algorithm development to predict plant and soil nutrition using satellite images. Moreover, a developed image recognition algorithm by his group is being used with modifications for agricultural land use applications in California. Dr. Panigrahi has successfully developed photonic sensing and electronic nose technologies for quality and safety of food products. For packaged meat, his group also developed multiple sensing methods including a portable electronic nose-based system for detection of spoilage and contamination.
This overall research has created positive impact in advancing engineering and sensor techniques for precision farming, agricultural production, food safety & quality applications. Many papers have been presented in conferences and published in referred journals including in the ASABE conferences and journals. He authored or co-authored two book chapters on imaging and sensing techniques for food products. Recently, he and his group have extended their expertise for environmental and health-care applications.
Selected Technical Papers and Publications:
(Dr. Panigrahi has contributed as author or co-author for more than 155 technical publications, papers, patents, book chapters and software).
Topics: Predictive health Informatics, Artificial intelligence, Deep learning
Deo, R. and S. Panigrahi. 2019. Prediction of Hepatic Steatosis (Fatty Liver) Using Machine Learning. International Conference on Computational Biology and Bioinformatics, Nagoya, Japan, October 2019. (In press).
Deo, R. and S. Panigrahi. 2019. Performance Assessment of Machine Learning Based Models for Diabetes Prediction. 2019 IEEE Healthcare Innovations and Point of Care Technologies Conference (HI-POCT), Bethesda, Maryland, USA, November 20 -22, 2019. (In press).
Xu, Ke and S. Panigrahi. 2019. Deep learning-based scene understanding model for assistive system related to Alzheimer’s patients. Proceedings of the International Society of Science and Applied Technology (ISSAT)’s International conference in Data Science and Intelligent Systems. August 1-3, 2019. Las Vegas, NV.
Panigrahi, S., X. Yu, and R. Deo. 2019. Instrumentation and Signal Processing Approaches for Vibration Analysis in Human Bio-system. The Vibration Institute 43rd Annual Training Conference, Lexington, KY, July 23-26, 2019. (Posters)
Editor/Book Chapters (Topics: Technology development, Sensors, Artificial intelligence, Environment and biological applications)
Panigrahi, S. 2018. Sustainable solution with Appropriate Technological Development and Innovation (SWADIN) workshop proceeding: Multidimensional Technological Innovations for Water-linked Health & Wellness. Purdue Press. In Press.
Panigrahi, S. and K. C. Ting. (Editors) 1998. Artificial Intelligence for Biology and Agriculture. Artificial Intelligence Review: An International Survey and Tutorial Journal. Vol.12. Nos. 1-3. February. 262 pages. Kluwer Academic Publisher. MA. ISSN NO. 0269-2821.
Panigrahi, S. and S. Gunasekaran. 2000. Optical Imaging and Sensing Techniques for Nondestructive Sensing of Food Products. In: Nondestructive Food Evaluation: Techniques to Analyze Properties and Quality. Marcel Dekker Inc. NY.
Gunasekaran, S. and S. Panigrahi. 2000. Fluorescence Techniques for quality evaluation of Food Products. In: Nondestructive Food Evaluation: Techniques to Analyze Properties and Quality. Marcel Dekker Inc. NY.
Schmoldt, D. and S. Panigrahi. 2008. “Biological Sensorics”: A special issue of the peer-reviewed journal “Biological Engineering”. Vol. 1, No. 2. May. (This special issue contained selected peer-reviewed papers that were presented at the International Biological Sensorics Conference that was held in 2007 at Minneapolis. I conceived the idea and was the lead co-organizer of the conference.)
Patents (Topics: Field Scale Sensors, Sensors and Sensing Systems)
Panigrahi, S and Q. Zhang. 2005. Optical Analyzer for grain. US Patent 6,845,326 B1 January 2005 (Conceived the idea, directed the research. I get 90% of the inventorship).
Panigrahi, S and Hofman, V. 2003. On-the-go sugar sensor for determining sugar content of sugar beets during harvesting- Part- 1 (hardware) US Patent No. 6,624,888 B2 issued Sept. 2003. (Conceived the idea; directed research. Most of the work and systems development was my contribution)
Panigrahi, S and Hofman, V. 2005. On-the-go sugar sensor for determining sugar content of sugar beets during harvesting - Part- II (Software) US Patent 6,851,662 B1 February 2005. (Conceived the idea; directed research. All most all the work on the software and algorithm development were my contribution).
Technical Publications (Topics: Technology development, Artificial intelligent technologies – machine learning, Computer vision, Sensing systems, Safety and biological applications, Predictive analytics)
Borhan, M., S. Panigrahi, M. A. Sattar and H. Gu. 2017. Evaluation of computer imaging techniques for predicting SPAD reading in potato leaves. Information Processing in Agriculture. 4(4): 275-282.
Mohapatra, P., S. Panigrahi and J. Amamcharla. 2015. Evaluation of a commercial electronic nose system coupled with universal gas sensing chamber for sensing indicator compounds associated with meat safety. J. Food Measurement and Characterization. 9(2): 121-129.
Mohapatra, P. and S. Panigrahi. 2012. Evaluation of surface enhanced Raman spectroscopy for detection of Acetone in the context of food safety and quality application. Journal of Food Research. 1(1):3-12.
Sankaran, S, L. Khot and S. Panigrahi. 2012.Biology and applications of olfactory sensing systems. A review. Sensors and Actuators B, 171-172. 1- 17.
Panigrahi, S., S. Sankaran, S. Mallik, B. Gaddam and A. Hanson. 2012. Olfactory receptor-based polypeptide sensor for acetic acid VOC detection. Materials Science and Engineering C. 32: 1307-1313.
Khot, L., S. Panigrahi, C. Doetkott, Y. Chang, J. Glower, J. Amamcharla, C. Logue and J. Sherwood. 2012. Evaluation of technique to overcome small dataset problems during neural-network based contamination classification of packaged beef using integrated olfactory sensor system. LWT – Food Science and Technology. Vol. 45. 233-240.
Balasubramanian, S, J. K. Amamcharla, S. Panigrahi, C. M. Logue. , M. Marchello, and J. Sherwood. 2012. Investigation of different gas-sensor-based artificial olfactory systems for screening Salmonella typhimurium contamination in beef. Food and Bioprocess Technology. 5(4): 1206-1219.
Sankaran, S and S. Panigrahi. 2012. Investigation on ZnO-Fe2O3 based nanocomposite sensors for butanol detection related to food contamination. Journal of Nanoscience and Nanotechnology. 12: 2346-2352.
Sankaran, S, and S. Panigrahi. 2011. Nanoparticulate zinc oxide chemoresistive sensor for volatile acetic acid detection. Journal of Nanosciene Nanotechnology Letters. 3, 755-762.
Gautam, R. S. Panigrahi, D. Franzen, and A. Sims. 2011. Residual soil nitrate prediction from imagery and non-imagery information using neural network technique. Biosystems Engineering. 110. 210-226.
Khot, L., S. Panigrahi, and D. Lin. 2011. Development and evaluation of piezoelectric polymer thin film sensors for low concentration detection of volatile organic compounds related to food safety applications. Sensors and Actuator B: Chemical. 153:1-10.
Sankaran, S., S. Panigrahi, and S. Mallik. 2011. Odor binding protein-based biomimetic sensor for detection of alcohols associated with Salmonella contamination in packaged beef. Biosensors and Bioelectronics. 26(2011)3103-3109.
Sankaran, S., S. Panigrahi, and S. Mallik. 2011. Olfactory receptor-based piezo electric biosensors for detection of alcohols related to food safety applications. Sensors and Actuator B: Chemical. 155(1): 8-18.
Amamcharla, J., S. Panigrahi. 2010. Application of vapor-phase Fourier transform infrared spectroscopy (FT-IR) and statistical feature selection methods for identifying S. Typhimurium contamination in beef. Biosystems Engineering. 107(1):1-9.
Amamcharla, J., S. Panigrahi, C. Logue, M. Marchello, and J. Sherwood. 2010. Fourier Transform Infrared Spectroscopy (FTIR) as a tool for discriminating Salmonella typhimurium contaminated beef. Sensing and Instrumentation for Food Quality and Safety. Vol. 4: 10-12. (Conceived the original idea, directed research work)
Bhattacharjee, P., S. Panigrahi, D. Lin, C. Logue, J. Sherwood, M. Marchello. 2010. A comparative qualitative study of the profile of volatile organic compounds associated with Salmonella contamination of packaged aged and fresh beef by HS-SPME/GC-MS. Journal of Food Science and Technology. 1(11):11-13.
Bhattacharjee, P., S. Panigrahi, D. Lin, C. M. Logue, J. S. Sherwood, C. Doetkott, and M. Marchello. 2010. Study of headspace gases associated with Salmonella contamination of sterile beef in vials using HS-SPME/GC-MS. Transaction of the ASABE. 53(1): 173-182.
Amamcharla, J. and S. Panigrahi. 2010. Simultaneous prediction of acetic acid/ethanol concentrations in their binary mixtures using metalloporphyrin based opto-electronic nose for meat safety applications. Sensing and Instrumentation for Food Quality and Safety. 4: 51-60.
Khot, L., S. Panigrahi, and P. Sengupta. 2010. Development and evaluation of chemoresistive polymer sensors for low concentration detection of volatile organic compounds related to food safety applications. Sensing and Instrumentation for Food Quality and Safety. Vol. 4 (1): 20 - 34.
Balasubramanian, S., S. Panigrahi, C. Logue, H. Gu, and M. Marchello.2009. Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification. Journal of Food Engineering. 91:91-98.
Khot, L., S. Panigrahi, and S. Woznica. 2008. Neural-network-based classification of meat: Evaluation of techniques to overcome small dataset. Biological Engineering. 1(2): 127-143.
Balasubramanian, S., and S. Panigrahi, C. Logue, C. Doetkott, and M. Marchello. 2008. Independent component analysis-processed electronic nose data for predicting Salmonella typhirium population in contaminated beef. Food Control. 19. 236-240.
Halley, S., G. Van Ee, V. Hofman, S. Panigrahi, and H. Gu. 2008. Fungicide deposition measurement by spray volume, drop size and sprayer system in cereal grains. Applied Engineering in Agriculture. 24(1): 15-21.
Gautam, R, and S. Panigrahi. 2007. Leaf nitrogen determination of corn plant using aerial images and artificial neural networks. 49. Canadian J. of Biosystems Engineering.
S. Balasubramanian, S. Panigrahi, B. Kottapalli, and C. Wolf-Hall. 2007. Evaluation of an artificial olfactory system for grain quality discrimination. LWT Food Science and Technology. 40. 1815-1825.
Gautam, R., S. Panigrahi, and D. Franzen. 2006. Neural network optimization of remotely sensed maize leaf nitrogen with a genetic algorithm and linear programming using five performance parameters. Biosystems Engineering, 95(3): 359-370.
S. Panigrahi, S. Balasubramanian, H. Gu, C. Logue, and M. Marchello. 2006. Design and Development of a metal oxide based electronic nose for spoilage classification of beef. Sensors and Actuators- B. 119. 2-14.
S. Panigrahi, S. Balasubramanian, H. Gu, C. Logue, and M. Marchello. 2006. Neural network integrated electronic nose system for spoilage identification of beef. Food Science and Technology/LWT. 39; 135-145.
Balasubramanian, S., S. Panigrahi, C. M. Logue, M. Marchello, and J.S. Sherwood. 2005. Identification of Salmonella inoculated beef using a portable electronic nose system. Journal of Rapid Methods and Automation in Microbiology. 13:71-95.
Balasubramanian, S., S. Panigrahi, C. Logue, M. Marchello, C. Doetkott, H. Gu, J. Sherwood, and L. Nolan. 2004. Spoilage identification of beef using an electronic system. Transactions of the ASAE. 47(5):1625-1633.
Borhan, M.S., S. Panigrahi, J. H. Lorenzen, and H. Gu. 2004. Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment. Transactions of the ASAE. 47(2):599-608.
Hong, Y, and S. Panigrahi. 2004. Image Processing techniques for processing of French fries. Applied Engineering in Agriculture 20(6): 803-811.
Gautam, R, and S. Panigrahi. 2003. Image processing techniques and neural network models for predicting plant nitrate using aerial images. Proceedings of the 2003 IEEE International Joint Conference on Neural Networks. Portland. Oregon. July 20-24. 2003.(Referred)
Chtioui, Y, S. Panigrahi, and L. Backer. 2003. Self-organizing map combined with a fuzzy clustering for color image segmentation of edible beans. Transactions of the ASAE. 46(3):831-838.
Zhang, Q, S. Panigrahi, S. Panda, and M. Borhan. 2002. Techniques for yield prediction for corn aerial images-A neural network approach. Journal of Agricultural and Biosystems Engineering. Vol. 3(1): 18-28.
Chtioui, Y, S. Panigrahi, and L. Backer. 1999. Rough sets theory as a pattern classification tool for quality assessment of edible beans. Transactions of the ASAE 42(4):1145-1154.
Chtioui, Y, L. J. Francl, and S. Panigrahi. 1999. Moisture prediction from simple micrometerological data. Phytopathology 89(8): 668-672. (directed the selection of statistical and neural network techniques)
Chtioui, Y., S. Panigrahi, and L. J. Francl. 1999. A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease. Chemometrics and Intelligent laboratory Systems. 48:47-58.
Chtioui, Y, S. Panigrahi, and R. March. 1998. Conjugate gradient and approximate Newton method for an optimal probabilistic neural network for food color classification. Optical Engineering. 37(11): 1-9. (Directed the research)
Francl, L, J. DeWolf, E.D., and S. Panigrahi. 1998. Utility of neural networks to plant disease epidemiology. Phytopathology. 88: S29.
Panigrahi, S. 1998. Neuro-fuzzy systems: Potential and applications in biology and agriculture. AI Application. 12. Nos.1-3: 83-95.
Francl, L., S. Panigrahi, and T. Padhi. 1997. Prediction of leaf wetness with neural network models. Agricultural and Forest Meteorology. 88: 57-65. (Contributed to the identification and development of neural network model)
Chtioui, Y, S. Panigrahi, and L. Francl. 1999. Neuro-regress. An interactive computer software for selected parametric and non-parametric (including neural networks) for chemometrics and prediction.
Chtioui, Y, and S. Panigrahi. 1999. Neuro-recognition. An interactive computer software for selected neural networks for classification and prediction.