Curriculum Vitae

Researcher | Electrical Engineer | Data Analyst | Academic

@Prabahar Ravichandran

Avatars

@Prabahar Ravichandran

Researcher

Plant Phenomics

Seven years of experience in managing and conducting research projects (experimental design, modeling, data analysis, and visualization) in the Agriculture and Agri-food sector; Developed computer vision systems with applications in plant phenomics, seed and grain quality

@Prabahar Ravichandran

Electrical Engineer

Industrial Automation

Two years of experience in industrial automation; design and commissioning control systems (DCS and PLC) in the agriculture sector (dairy processing, greenhouses, and food processing industries); Over two years of experience in developing embedded systems for energy monitoring in energy management systems at farms

@Prabahar Ravichandran

Data Analyst

Experimental Design and Data Analysis

Designed experiments for agricultural research projects; Developed models for analyzing complex phenomics and genomics datasets the require intensive computation using Python and R;

@Prabahar Ravichandran

Academic

Electrical Engineering and Computer Science

Eight years of university teaching experience (eight different courses in electrical engineering and computer science discipline); Presented over 600 lectures as a 'Part-time Academic'

Work Experience

  • Characterized herbicide symptomology in model weed species (tame oats and oriental mustard) using UAV hyperspectral and multispectral imaging
  • Estimated optimal desiccation window & dry-down performance of desiccants in small red lentils
  • Identified resistant and susceptible weed (Kochia and Wild oat) biotypes with Proximal and UAV hyperspectral and multispectral profiling
  • Developed machine learning and deep learning models using Python (for ML and DL) and R (for statistical modelling and data visualization) to estimate herbicide efficacy/phytotoxicity
  • Created orthomosaics, Digital Surface Models(DSM), and point cloud with commercial (Pix4d mapper, ArcGIS Pro, ENVI) and open-source (WebODM, QGIS) GIS applications
  • Managed structured and unstructured high-throughput phenotyping (HTP) data with SQL (PostgreSQL & MySQL) and No SQL (Mongo DB) database management systems
  • Calibrated FOSS NIRS (Near-Infrared Spectroscopy) for amylose and chalkiness estimation in rice
  • Estimated rice blast severity using image processing and convoluted neural network (CNN)
  • Detected chaffy grains in rice using images and object detections (YOLO) algorithms
  • Developed CNN to recognize NLB (Northern Leaf Blight) in susceptible genotypes using proximal field images
  • Developed machine learning algorithms for Genome-Wide Association Studies (GWAS) and compared its performance with conventional models
  • Predicted vigor and viability of rice seeds using Computed Tomography (CT)

  • Thermo-Fluids 1: Review of the fundamental laws of thermodynamics, introductory psychrometry and analysis of the ideal gas compressor cycle, Rankine cycle, Otto cycle, Diesel cycle, Brayton cycle and vapor compression refrigeration cycle
  • Engineering Economics: Capital, cash flow, and the time value of money concepts. Nominal and effective interest rates when considering loans, mortgages, and bonds. The application of present worth analysis, annual equivalent analysis, and rate of return analysis in evaluating independent projects, comparing mutually exclusive projects, analyzing lease vs. buy alternatives, and making decisions
  • Digital electronics and computer interfacing: Fundamentals of electrical engineering; electronic devices; sensor; semiconductor devices; logic gates; AutoCAD TinkerCAD; ANSI C, hands-on projects with Arduino Uno, LDRs, temperature and humidity sensors, relays, stepper motors, seven-segment displays, and LCDs
  • Energy conversion and assessment: Demand side management – energy efficiency and conservation; Energy conversion – conventional and non-conventional sources; renewable energy technologies – evaluation and assessment
  • Energy production and utilization: Power system - generation, transmission, and distribution; electrical machines; process control – PLC (Programmable Logic Control), PID controllers; Electrical code (CSA C22.1 2015 & NFPA 70 2017)
  • Communication technology: Photography- digital cameras, lenses and accessories, composition, and photo editing; videography – video editing techniques; graphic design – illustrations, posters, magazines, and websites; Adobe photography and design
  • Computer Science: Microcontroller and microprocessors; C-programming – conditional statements, loops, functions, pointers, and file handling; Arduino programming and applications with LDRs, piezoelectric transducers, temperature, and proximity sensors
  • Computer Methods: Microsoft Word - typography, layouts for posters and articles, reference managers; Microsoft Excel - creating graphs, tables, and using macros; Microsoft PowerPoint - Basic presentation, e-learning; ; Internet and web design - HTML, CSS, JavaScript, website builders

  • Designed data-logging devices for monitoring energy use in residential and small industrial applications
  • Expert in soldering SMD (Surface Mount Devices) and thru-hole components
  • Designed transformer-less power supplies, and currents and voltages sensing circuits
  • Programmed PICs and ATMEL micro-controllers using C and assembly language
  • Proficient with Cadence OrCAD and Allegro PCB Editor
  • Retrieving and storing data from EEPROMS, USB flash drives, and SD cards
  • Familiar with USART, SPI, I2C, USB, Zigbee, and Wi-Fi communication protocols
  • Managed finances and lab resources

  • Trained on Yokogawa DCS (Distributed Control System) and PLC (Programmable Logic Control)
  • Developed a distributed control system for the dairy processing industry
  • Commissioned and optimized a cheese processing unit and pouch packaging system in a dairy processing facility

Education

  • Trained deep learning neural networks on Compute Canada’s high-performance computing cluster
  • Developed deep learning models for grain quality parameters estimation using Near-InfraRed Spectroscopy (NIRS)
  • Developed a model to estimate blast severity in rice with deep learning neural networks
  • Deployed Django-based web applications for processing images using deep learning models
  • Implemented a real-time deep learning image processing system on NVIDIA Jetson Single Board Computers (SBCs)

  • Performed energy audits (ASHRAE level 1 - 20%, 2 - 60% and 3 - 20%) on over 30 farms
  • Installed long-term energy monitoring systems on four greenhouse operations
  • Tested biomass boilers for particulates and NOx and SO2 emissions using EPA testing methods
  • Tuned furnace oil and biomass boilers in commercial greenhouse operations for optimal performance
  • Produced pellets and briquettes from perennial grass, agricultural and forest residues
  • Analyzed solid fuel properties according to ASTM standards - proximate and ultimate analysis, alkali metal content (AAS - Atomic Absorption
    • Spectroscopy), and energy content using a bomb calorimeter
  • Optimized biodiesel production using response surface methodology
  • Have published two peer-reviewed research articles

  • Completed the structured confirmatory exam program (National Technical Exams) conducted by Engineers Canada to demonstrate my knowledge and academic qualifications
  • Developed a DCS for the dairy processing industry using Yokogawa’s Centum CS 3000

Publications

    Abstract

    The study explores the use of LiDAR data for canopy height estimation, comparing conventional and deep learning methods. The conventional approach, which utilizes percentile and percentage methods for ground and canopy height extraction, indicates that focusing on the upper parts of the canopy yields better predictions of canopy height. In contrast, the deep learning approach demonstrates significant effectiveness in predicting canopy height, with a CNN model showing a high R-squared value of 82.40%, underscoring the potential of advanced preprocessing and machine learning techniques in enhancing the accuracy of height estimation from LiDAR data.

    Abstract

    Nitrogen (N) fertilizer is one of the essential plant nutrients optimized to enhance crop yield and quality in modern agricultural fields. The conventional practice of applying N in a single dose before planting or in a random split dose without having precise knowledge of the amount and timing of crop N requirements has led to excessive use of N fertilizers in Canadian agriculture. Excess N application is a serious environmental issue having a huge impact on greenhouse gas emissions, soil health, and water resources (Xiong, Chen et al. 2015). Optimal N utilization is critical to maximize yield and mitigate the environmental impacts of run-offs from agricultural fields (Yang, Du et al. 2020, Jiang, Zhu et al. 2021). Leaf N content is an important indicator of photosynthetic efficiency (Jung, Song et al. 2021). It could assist agriculturists in better managing N applications at the early vegetation stages, that impact the yield in the later stages of the potato growth cycle.

    In this study, a commercial potato cultivar, Russet Burbank was grown to generate a prediction model on in-season N fertilizer application based on actual crop N requirements. We estimated the leaf nitrogen content with machine-learning models using spectral data obtained from SpectraVue leaf spectrometer. The treatments include an untreated control with nitrogen applied at five different rates (47.5, 95, 142.5, 190, and 237.5 kg/ha-1). The leaf spectral profiles (360 – 1100 nm) were obtained at the vegetative, tuber initiation, and maturity stages. Right after the spectral profiles were acquired, the leaves were sampled to obtain the leaf nitrogen content using Partial least squares regression models were developed to estimate the leaf N content from the spectral profiles. The developed models were able to estimate leaf N content with an R2 (coefficient of determination) of 78.6%. Increasing the number of observations could minimize the error and further improve the prediction accuracy.

    Abstract

    Kochia (Bassia scoparia) is an invasive broadleaf weed species that has been reported to be responsible for up to 90% yield losses in major field crops. Herbicides are primarily being used to control Kochia. However, in recent years the incidence of herbicide- resistant kochia has been increasing in North American prairies. There is a pressing need for a technique to recognize herbicide-resistant kochia biotypes for the susceptible ones to promptly suggest an alternative course of action. In this study, we assessed two different herbicides (Glyphosate and Fluroxypyr) with 6 different kochia populations each. Hyperspectral imagery of the treated kochia seedling was obtained on the day of treatment along with 1 and 3 days after treatment. The classification models that were built to identify resistant and susceptible kochia biotypes were able to classify with an accuracy of 75.11% for Glyphosate and 82.14% for Fluroxypyr.

    Abstract

    The use of herbicides is one of the predominant methods often deployed to restrict weeds. Over time, weeds mutate and develop resistance against certain herbicides, hence it is essential to screen for herbicide’s efficacy on evolving weed populations. This study aims to utilize proximal hyperspectral imaging to estimate crop injury from herbicide applications in tame oat [Avena sativa; model species for wild oat (Avena fatua)] and oriental mustard [Brassica juncea; model for wild mustard (Sinapis arvensis)]. The treatments included an untreated control along with 8 herbicides at their recommended dose for mustard and an untreated control along with 6 herbicides at their recommended dose for oat. The experiment was conducted at Lethbridge, AB, Canada. The imagery and the visual control rating were obtained at 2 different time points for hyperspectral imaging (HSI). The regression models developed with hyperspectral images were able to estimate crop phytotoxicity with an R2 (determination of coefficient) of 90.93% and 71.80%.

    Abstract

    Herbicides have become indispensable in modern agricultural production, especially in Western Canada. Extensive herbicide use has led to an increasing number of herbicide-resistant weeds. Hence, it is imperative to constantly screen herbicide efficacy on evolving weed populations(Beckie et al. 2020). Plants exhibit a range of responses to herbicides with differing modes of action, including chlorosis, necrosis, stunting, epinasty, height reduction, biomass reduction, and mortality. Although these symptoms could be rated individually, they are cumulatively assessed on a 0 – 100 visual rating scale for crop phytotoxicity(“CWSS_SCM Rating Scale - Canadian Weed Science Society/Société Canadienne de Malherbologie” 2022). Visual assessment for crop phytotoxicity and herbicide efficacy, although subjective, is the accepted standard used by pesticide manufacturers and the Pest Management Regulatory Agency in Canada. However, with the advent of hyperspectral imaging and machine learning, a novel, robust, non-subjective, and cost-effective evaluation system could be developed(Singh et al. 2020; Duddu et al. 2019). In this study, we propose the use of proximal hyperspectral imaging to estimate crop injury/phytotoxicity in tame oat [Avena sativa; model for wild oat (Avena fatua)] and oriental mustard [Brassica juncea; model for wild mustard (Sinapis arvensis)].

    Abstract

    Timely harvest is important with lentils, especially since harvest timing is a compromise between increasing yield from younger pods and losses due to shattering of mature pods (Singh et al. 2020). Chemical desiccation has been proven to be very effective for lentils (Zhang, Johnson, and Willenborg 2016). Since chemical desiccation has an immediate dry-down effect, it is essential to ensure the crops are at maturity. Application of desiccation early would reduce the seed size and yield of the lentil crop.

    Multispectral imaging has been used as a promising alternative for herbicide tolerance studies (Duddu et al. 2019; Singh et al. 2020). In this study, we used UAV multispectral imaging to estimate plant moisture to determine the optimal window for desiccation. In addition, following application of the desiccant, dry-down efficiency was assessed through visual ratings of the five conventional and two organic desiccants.

    Abstract

    Background and Objectives:
    Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality-related traits are often ignored as they are arduous and time-consuming. Near-infrared spectroscopy (NIRS) could be a suitable high-throughput alternative to conventional wet chemistry and image processing-related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification.
    Findings:
    Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer-learning capabilities, the time required for model development could be significantly reduced.
    Conclusion:
    We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension-related traits such as length and length/width ratio.
    Significance and Novelty:
    We found NIRS to be an appropriate alternative to wet chemistry and image-based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high-throughput applications.

    Abstract

    Blast is a fungal disease caused by Magnaporthe oryzae that affects different parts of the rice plant in all development stages. In the late 20th century, this catastrophic widespread disease was responsible for a loss of between 10% to 30% in total production every year. Cultivars produced through conventional, cisgenic, or transgenic breeding are evaluated for resistance against leaf blast in a Uniform Blast Nursery (UNB). UBN simulates a warm and humid environment conducive for the fungus to thrive. Once the seedlings are established, about 21 days after sowing they are inoculated with a spore suspension. The symptoms are assessed based on the lesion size and the percentage of leaf area diseased between 5 to 7 days after inoculation. The disease symptoms are evaluated on a scale from 0 to 9 according to the Standard Evaluation Scale proposed by International Rice Research Institute (IRRI). This study aims to develop a U-Net-based semantic segmentation model that was built to lay out a few novel indices and pixel area parameters that quantify blast disease severity for assessment and validation. A total of 564 proximal images acquired before scoring were used for analysis. The model to segment lesions along with infected and uninfected plant pixels in the images was developed with a Dice coefficient of 0.96. Indices comparing the extracted pixel parameters were able to classify with an accuracy of 87.13%. The model performance predominantly declined due to the misclassification of images with scores 7 to 9 that are classified as susceptible.

    Abstract

    This paper presents the results of an investigative study to determine the impact of leaching agricultural biomass on gaseous emissions and total suspended particles. Gaseous emission and total suspended particulates were measured in the stack of a domestic wood stove fired with leached and un-leached briquettes produced from commonly available agricultural biomass feedstock grown in Nova Scotia, Canada. The study primarily focuses on reducing gaseous and particulate emissions by leaching the biomass feedstock with water, prior to conversion and comparing against un-leached biomass feedstock. The result showed that the process of leaching significantly improved the fuel properties of the feedstock; however, a proportional reduction in gaseous and particulate matter emissions was not evident

    Abstract

    Agricultural biomass presents a promising feedstock, which may contribute to a transition to low carbon fuels. A significant amount of research has identified a number of challenges when combusting agricultural feedstock, related primarily to energy value, ash, emissions, corrosion and combustion characteristics. The mitigation of such challenges can be addressed more cost effectively when dealing with large or utility scale combustion. The costs associated with harvesting, conversion, transportation and ultimately, market development all create additional roadblocks for the creation of an agricultural biomass industry. Nova Scotia, an Eastern Canadian province, has significant land resources, however it is prone to wet spring and as yet does not have a supply chain established for such an industry. The main components of supply, processing and conversion and demand simply do not yet exist. This research addresses one aspect of this supply chain by attempting to develop a fuel suitable for a) existing markets (local residential wood and wood pellet stoves and b) a scale that will support industry engagement. The outcomes of this research have determined that such a venture is possible and presents empirical preprocessing conditions to achieve a competitive agricultural fuel.