Dr. Pappu K. Yadav Profile

Dr. Pappu K. Yadav

Asstistant Professor at South Dakota State Univ

SPIE Involvement:

Author

I am an Assistant Professor of Precision Agriculture AI Engineering in the Department of Agricultural & Biosystems Engineering at South Dakota State University, Brookings, SD. Prior to this, I was a Post-Doctoral Research Associate in the Department of Agricultural & Biological Engineering at University of Florida, Gainesville, FL. I completed my Ph.D. in Biological & Agricultural Engineering at Texas A&M University, College Station, TX. My areas of research lie at the intersection of remote & proximal sensing using multispectral & Hyperspectral imaging, precision agriculture, artificial intelligence, uncrewed aerial systems, and agricultural robotics.

Proceedings Article | 7 June 2024 Presentation + Paper

A deep learning approach for precision phenotyping of corn stomata

Sainath Reddy Gummi, Chulwoo Pack, Pappu Yadav, Young Chang, Mazhar Sher, James Kemeshi, Mohammad Ashik Alahe

Proceedings Volume 13053, 1305309 (2024) https://doi.org/10.1117/12.3013894

KEYWORDS: Object detection, Education and training, Deep learning, Microscopes, Tolerancing, Data modeling, Nondestructive evaluation, Agriculture, Vehicle sensing, Robotics

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Proceedings Article | 7 June 2024 Presentation + Paper

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Quentin Frederick, Thomas Burks, Pappu Kumar Yadav, Jianwei Qin, Moon Kim, Megan Dewdney

Proceedings Volume 13053, 130530C (2024) https://doi.org/10.1117/12.3021207

KEYWORDS: Diseases and disorders, Cameras, Principal component analysis, Reflectivity, Inspection, Spatial resolution, Hyperspectral imaging, Image classification

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Proceedings Article | 6 June 2024 Presentation + Paper

Detection of citrus black spot fungus using fluorescence imaging and deep learning on leaf surface

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Megan Dewdney, Mark Ritenour, Fartash Vasefi

Proceedings Volume 13060, 1306003 (2024) https://doi.org/10.1117/12.3013510

KEYWORDS: Fluorescence imaging, Education and training, Diseases and disorders, Deep learning, Imaging systems, Light emitting diodes, Fluorescence, Organization management, Matrices, Agriculture

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Proceedings Article | 6 June 2024 Presentation + Paper

Detection of E. coli concentration levels using CSI-D+ handheld with UV-C fluorescence imaging and deep learning on leaf surfaces

Pappu Kumar Yadav, Thomas Burks, Snehit Vaddi, Jianwei Qin, Moon Kim, Mark Ritenour, Fartash Vasefi

Proceedings Volume 13060, 1306006 (2024) https://doi.org/10.1117/12.3014017

KEYWORDS: Image classification, Fluorescence, Deep learning, Education and training, Light emitting diodes, Fluorescence imaging, RGB color model, Discrete wavelet transforms, Cameras, Wavelet transforms

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The transmission of Escherichia coli (E. coli) bacteria to humans through infected fruits and vegetables, such as citrus, can lead to severe health issues, including bloody diarrhea and kidney disease (Hemolytic Uremic Syndrome). Therefore, the implementation of a suitable sensor and detection approach for inspecting the presence of E. coli colonies on fruits and vegetables would greatly enhance food safety measures. This article presents an evaluation of SafetySpect's Contamination, Sanitization Inspection, and Disinfection (CSI-D+) system, comprising an UV camera, an RGB camera, and illumination at fluorescence excitation wavelengths: ultraviolet C (UVC) at 275 nm. To conduct the study, eight different concentrations ranging from 10⁰ (control) to 10⁸ (maximum) cell counts of bacterial populations were inoculated on extracted citrus peel specimens. Specimen data could represent either irrigation or sprayer-based contamination events or direct contact with wildlife. Our study delves into early detection using the portable CSI-D+ system, capturing 240x240 pixel UV-C fluorescence images of E. Coli-inoculated grapefruit peel plugs. We developed a pipeline to prepare these images for the YOLOv8 deep learning framework, facilitating E. coli classification across varying concentrations and backgrounds. To enhance explainability, we employed Eigen Class Activation Map (Eigen-CAM) with YOLOv8, utilizing 'pytorch-eigen-cam' (https://github.com/rigvedrs/YOLO-V8-CAM) to elucidate the model's decision-making in detecting and classifying different E. coli concentrations. Our study demonstrated that the CSI-D+ system could classify fluorescence images at eight different concentration levels with an overall accuracy of more than 83% in which the control class reached a perfect classification accuracy while the images with E. coli concentration of 10⁶ CFU/drop had the lowest accuracy of 71%. Similarly, the images with maximum concentration i.e., 10⁸ CFU/drop were classified at an accuracy of 94%. These findings demonstrate the application of the CSI-D+ system as a rapid, non-invasive tool for E. coli detection on citrus peel surfaces that may be on the tree thus alerting the potential for similar contamination on fruit still on the tree. By providing timely insights, these results could enable effective intervention strategies to eliminate dangerous E. Coli from the food chain.

SPIE Journal Paper | 8 February 2024 Open Access

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour

JARS, Vol. 18, Issue 01, 014512, (February 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.014512

KEYWORDS: Diseases and disorders, Education and training, Image classification, Reflectivity, Matrices, Classification systems, Principal component analysis, Hyperspectral imaging, Cameras, Machine learning

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Proceedings Article | 13 June 2023 Presentation + Paper

Classifying E. coli concentration levels on multispectral fluorescence images with discrete wavelet transform, deep learning, and support vector machine

Pappu Kumar Yadav, Thomas Burks, Quentin Frederick, Jianwei Qin, Moon Kim, Mark Ritenour, Kunal Dudhe

Proceedings Volume 12545, 1254508 (2023) https://doi.org/10.1117/12.2663933

KEYWORDS: Education and training, Image classification, Discrete wavelet transforms, Deep learning, Machine learning, Fluorescence, Contamination, Wavelets, Lithium, Biofilms

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Escherichia coli (E. coli) bacteria when transmitted to humans by infected fruits like citrus can cause serious illnesses like bloody diarrhea and kidney disease (Hemolytic Uremic Syndrome). Therefore, inspection for the presence of E.coli colonies on fruits and vegetables would be beneficial for food safety if an appropriate sensor and detection approach were available. In this paper, we evaluate the efficacy of SafetySpect’s Contamination, Sanitization Inspection, and Disinfection (CSI-D+) system, which consists of an UV camera, an RGB camera, and illumination at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm, respectively. For this study, bacterial population at four different concentration levels were inoculated on black rubber slides. The black rubber slides provided a fluorescence-free background for benchmark tests on E.coli-containing droplets, since even fruit peels have a subdued fluorescence response which appears as low intensity noise in the background of the UV images. To increase the number of image datasets to avoid overfitting issue of deep leaning and machine learning models, synthetic datasets were generated using StyleGAN2- ADA generative network. As a first approach, the fluorescence images were denoised using discrete wavelet transform (DWT) and reconstructed denoised images were used to train StyleGAN2-ADA for generating larger datasets which were later used by VGG19 with SoftMax classifier. In the second approach, VGG19 was used to extract features and then Linear and RBF SVM were used for classification. It was found that using Symlets family of DWT denoised images at an average PSNR value of 25.59 dB. VGG19 with SoftMax was able to classify the images at overall accuracy of 84% without synthetic dataset and at 94% by using augmented dataset generated by StyleGAN2-ADA. It was also found that using RBF SVM increased the accuracy by 2% to 96% and linear SVM by 3% to 97%. The findings reported here could be used to detect presence of E. coli bacterial population on citrus peels for taking the necessary actions for decontamination.

Proceedings Article | 13 June 2023 Presentation + Paper

Citrus disease classification with convolution neural network generated features and machine learning classifiers on hyperspectral image data

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan Dewdney, Mark Ritenour

Proceedings Volume 12539, 1253902 (2023) https://doi.org/10.1117/12.2665768

KEYWORDS: Diseases and disorders, Image classification, Reflectivity, Principal component analysis, Convolution, Classification systems, Hyperspectral imaging, Imaging systems, Deep learning, Hyperspectral systems

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Citrus black spot (CBS) is a quarantine fungal disease caused by Phyllosticta citricarpa that can limit market access for fruit. It causes lesions on fruit surfaces and may lead to premature fruit drops, reducing yield. Leaf symptoms are uncommon for CBS, although the fungus reproduces in leaf litter. Similarly, citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and leads to economic losses for growers from fruit drops and blemishes. Therefore, early detection and management of groves infected by CBS or canker via fruit and/or leaf inspection can benefit the Florida citrus industry. Manual inspection to classify disease symptoms on either fruits or leaves is a tedious and labor intensive process. Hence, there is need to develop computer vision system for autonomous classification of fruits and leaves that can speed up their management in fields. In this paper, we demonstrate the capability of convolution neural network (CNN)-based deep learning along with classical machine learning (ML) based computer vision algorithms to classify ‘Valencia’ orange fruit surfaces with CBS infection along with four other conditions and ‘Furr’ mandarin leaves with canker and four other conditions. Fruits with CBS and four other conditions (marketable, greasy spot, melanose and wind scar) were classified using a custom shallow CNN with SoftMax and RBF SVM at an overall accuracy of 89.8% and 92.1%, respectively. Similarly, a custom VGG16 network with SoftMax could classify canker leaves with F1-score of 85% and overall accuracy of 82% including other four conditions (control/healthy, greasy spot, melanose and scab). In addition, it was found that by replacing SoftMax with RBF SVM in the VGG16 network, the overall classification accuracy improved to 93% i.e., an increment of 11% points (13.41%). The preliminary findings reported in this paper demonstrate the capability of HSI system for automated citrus fruit and leaf disease classification using shallow and deep CNN generated features and ML classifiers.

Proceedings Article | 4 June 2022 Presentation + Paper

Cotton crop disease detection on remotely collected aerial images with deep learning

Quandong Qian, Kevin Yu, Pappu Kumar Yadav, Sambandh Dhal, Stavros Kalafatis, J. Alex Thomasson, Robert Hardin

Proceedings Volume 12114, 1211405 (2022) https://doi.org/10.1117/12.2623039

KEYWORDS: Algorithm development, Global Positioning System, Airborne remote sensing, Multispectral imaging, Image processing, Optimization (mathematics), Evolutionary algorithms, Cameras, Agriculture, Matrices

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Proceedings Article | 4 June 2022 Presentation + Paper

Using under-canopy cotton imagery for cotton variety classification

Hussein Gharakhani, J. Alex Thomasson, Peyman Nematzadeh, Pappu Kumar Yadav, Steve Hague

Proceedings Volume 12114, 121140B (2022) https://doi.org/10.1117/12.2623034

KEYWORDS: Unmanned ground vehicles, RGB color model, Image classification, Cameras, Agriculture, Data acquisition, Image processing, Unmanned aerial vehicles

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Proceedings Article | 4 June 2022 Presentation + Paper

Volunteer cotton plant detection in corn field with deep learning

Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin, Stephen Searcy, Ulisses Braga-Neto, Sorin Popescu, Daniel Martin, Roberto Rodriguez, Karem Meza, Juan Enciso, Jorge Solorzano, Tianyi Wang

Proceedings Volume 12114, 1211403 (2022) https://doi.org/10.1117/12.2623032

KEYWORDS: RGB color model, Agriculture, Inspection, Data processing, Computer architecture, Cameras, Algorithm development, Soil science, Silver, Performance modeling

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Proceedings Article | 14 May 2019 Presentation + Paper

Assessment of different image enhancement and classification techniques in detection of volunteer cotton using UAV remote sensing

Pappu Kumar Yadav, J. Alex Thomasson, Juan Enciso, Sayantan Samanta, Amrit Shrestha

Proceedings Volume 11008, 110080M (2019) https://doi.org/10.1117/12.2518721

KEYWORDS: Image classification, Image filtering, Mahalanobis distance, Unmanned aerial vehicles, Near infrared, Image processing, Accuracy assessment, Cameras, Global Positioning System, RGB color model

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