Young Ki Chang

Development of Color Co-occurrence Matrix Based Machine Vision Algorithms for Wild Blueberry Fields

Co-occurrence matrix-based textural features were analyzed and three algorithms were developed to identify bare spots, wild blueberry plants, and weeds with the aim of applying agrochemicals to wild blueberry cropping fields in a spot-specific manner. Images were acquired using four cameras and a ruggedized laptop with custom-written programs coded in Microsoft Visual® C++. Textural features were extracted from the images using MATLAB® and analyzed with SAS®. Forty-four textural features were extracted from co-occurrence matrices of NTSC luminance (L), hue, saturation, and intensity (HSI) images. Multiple discriminant analysis using all 44 features (DF_ALL model) showed 98.1% of overall classification accuracy and 83 ms of processing time of an image with C++ calculation. Based on the results of multiple discriminant analysis and two-step linear discrimination plotting, the DF_HSISD, DF_SISD, and HSILD algorithms are preferred algorithms with overall accuracy of 94.9%, 92.7%, and 91.4%, and processing time of 55, 27, and 29 ms, respectively. Any of three reduced textural feature algorithms can be employed for spot-specific application of agrochemicals in wild blueberry cropping fields. The choice of one algorithm over another will depend on whether processing speed or accuracy is more important for the end-user’s application.

Performance Evaluation of a Prototype Variable Rate Sprayer for Spot – Specific Application of Bravo® Fungicide in Wild Blueberry

Wild blueberry yields are highly dependent on fungicides to control floral blight (monilinia and botrytis) and leaf diseases (septoria and rust). Growers apply fungicides uniformly without considering the significant bare spots (30-50% of the total field area). The repeated and excessive use of agrochemicals in bare spots has resulted in an increased cost of production. The over use of the fungicides is harmful to the environment and can contaminate surface and ground water. The proper targeting of fungicide on only foliage has the potential to save a substantial amount of chemical. Therefore, a significant need of an affordable variable rate sprayer for spot application of fungicide is needed for wild blueberry production. The objective of this study was to determine the performance of a prototype variable rate sprayer for spot application of Bravo® fungicide in wild blueberry fields. The 6.1 meter VR sprayer was mounted on a all-terrain vehicle. Color cameras were used to detect bare soil areas in wild blueberry fields. Eighteen 6.1 meter wide test plots were selected in a wild blueberry field and the bare soil areas were mapped using RTK-DGPS. Three application rates (uniform, variable and control) were selected at random for each plot. Digital color images were taken at 6 randomly selected locations in each of the 18 plots. Each image was analyzed to calculate green ratio for determining effect of Bravo® on wild blueberries. The application tracks were statistically compared with reference to the control tracks. Water sensitive paper was also placed in randomly selected locations for analysis purposes. The results can be used to determine the performance of applying fungicide on site-specific bases using a variable rate sprayer.

An Automated Headspace Sampling system for Meat Safety

In case of meat contamination studies, the volatile organic compounds (VOCs) generated by microorganisms provide important information about the presence of pathogenic bacteria in meat. Our current meat contaminations experiments have used headspace output of meat packages at 24 h intervals. However, it is also important to observe the VOC pattern change at every 3-4 h interval. This paper describes a specially designed automated headspace sampler to extract headspace from control and Salmonella spiked meat at user defined time intervals (4h, 8h and 12h) under anaerobic conditions. The collected headspace samples were analyzed by Gas Chromatography/Mass Spectroscopy (GC/MS) and TF-module electronic nose sensor.

An on-the-go ultrasonic plant height measurement system (UPHMS II) in the wild blueberry cropping system

Abstract. Wild blueberry ( Vaccinium angustifolium Ait.) is a perennial rhizomatous low shrub and mostly mechanically harvested. The operator of the harvester needs to maintain the optimum height of harvester‘s head according to the plant height for better yield and quality while decreasing plant pulling. An ultrasonic on-the-go plant height measurement system (UPHMS II) was developed and compared with previous height measurement system (UPHMS I). A real-time kinematics differential global positioning system (RTK-GPS), a custom program and a ruggedized computer and both plant height sensing system were mounted on a commercial mechanical harvester for real-time plant height measurement during harvesting. A custom program was developed to acquire and process ultrasonic sensing data in real-time from both UPHMS I and UPHMS II simultaneously. Two wild blueberry fields were selected in central Nova Scotia to evaluate the performance of both UPHMS I and UPHMS II. Manually measured plant height values from 24 plots were compared with real-time measured values of two systems. UPHMS II performed better to predict wild blueberry plant height with higher accuracy than UPHMS I. The RMSE of the sensed height of UPHMS I and UPHMS II were 6.4 cm and 1.6 cm, respectively. UPHMS II can be an economic option to control wild blueberry harvester head automatically to increase harvester and operator‘s efficiency. Real-time and accurate sensing of plant height is the first step toward the automation of the wild blueberry harvester. Refinements for the optimum head height according to the plant height is required for future studies.

PREDICTIVE MODEL FOR WILD BLUEBERRY FRUIT LOSSES DURING HARVESTING

Abstract. Wild blueberries are one of the most important fruit crops of Canada, producing more than 50% of the world’s production. Understanding and predicting the relationships between the machine operating parameters, fruit losses, topographic features and crop characteristics can aid in better berry recovery during mechanical harvesting. This paper suggested a modeling approach for prediction of fruit losses during harvesting using artificial neural network (ANN) and multiple regression (MR) techniques. Four wild blueberry fields were selected and completely randomized factorial (3 x 3) experiments were constructed at each site. One hundred sixty two plots (0.91 x 3 m) were made at each site, in the path of operating harvester. The total fruit yield, total losses were collected from each plot within selected fields. The harvester was operated at specific levels of ground speed (1.20, 1.60 and 2.00 km h-1) and head rotational speed (26, 28 and 30 rpm). The readings of slope, plant height, and fruit zone were also recorded from each plot. The collected data were normalized, and 70% of the data were utilized for training, and 30% for validation of the developed models using ANN and MR techniques. The developed models were validated internally and externally and the best performing model was identified based on mean square error (MSE), root mean square error (RMSE), coefficient of efficiency (CE) and coefficient of determination (R2). Results of scatter plot among the RMSE and epoch suggested that an epoch size of 15000 was appropriate to process fruit losses using ANN approach. Results revealed that the prediction accuracy of the MR models was lower (R2 = 0.46; RMSE = 0.14) than the ANN model (R2 = 0.84; RMSE = 0.075) for training dataset, which might be due to the non-linear nature of the data. Results reported that the ANN model predicted fruit losses with higher (R2 = 0.63; RMSE = 0.11) accuracy when compared with MR model (R2 = 0.37; RMSE = 0.15) for external validation dataset. Overall, the results of the study suggested that the ANN model was able to predict fruit losses accurately and reliably as functions of fruit yield, crop and machine variables. This study will help to identify the factors responsible for fruit losses and to suggest optimal harvesting scenarios to improve berry picking efficiency and recovery.

Development and Performance Testing of a Light Source System on a Smart Sprayer for Spot-Application of Agrochemical in Wild Blueberry Fields

Abstract. Wild blueberry producers occasionally are required to apply agrochemicals during the early morning, evening or after dark with low wind conditions. The objective of this study was to develop an artificial light source system that could be added to a smart sprayer to allow cameras to detect target areas in the field with low ambient light conditions. The design requirements were a rugged construction that gave an even light distribution under an entire 12.2 m machine vision sensor boom. Polystyrene diffuser sheets were used to eliminate the hot spots created by the lights. A lux light meter was used to determine the light intensity at 0.3 m spacing on the ground under the camera boom with zero ambient light. A field test was completed in a wild blueberry field in central Nova Scotia, Canada to test the developed light source system with low natural light conditions. A real-time kinematics-global positioning system was used to map the boundary of the test track, selected bare soil areas, weed areas and wild blueberry plant areas in the field. The smart sprayer and light source system was driven across the test track several times using different combinations of camera and image processing settings to determine the optimum values for use with the developed light source. Spray percent area coverage on water sensitive papers placed in bare soil and blueberry patches were 22.34% and 25.79% lower than in weed patches, respectively. Spray savings of 65% was obtained using the smart sprayer for spot-application on weeds.

Development of Commercial Prototype Variable Rate Sprayer for Spot- Application of Agrochemicals in Wild Blueberry

Wild blueberry growers apply agrochemicals uniformly to control weeds within fields. The repeated and excessive use of agrochemicals in bare spots that exist within fields and on plants has resulted in increased cost of production and polluted environment. A commercial prototype variable rate (VR) sprayer was developed for spot-application of agrochemicals in a specific section of the 12.2 m sprayer boom where the weeds have been detected. The boom was divided into 16 sections (97 cm each section). VR control system consisted of eight digital color cameras mounted on a separate boom in front of the tractor, 20-channel MidTech Legacy 6000 controller and two 8-channel VR controllers interfaced to a Pocket PC using wireless Bluetooth® radio. Cameras were attached using USB serial cables to the computer. Custom software was capable of processing the images to detect weeds in real-time, and weed triggering signals were sent to the VRC to spray in the specific boom section where the weeds have been detected.

Performance of Polyvinylphenol-Carbon Black Composite as Ethanol Sensor for Food Safety Applications

Ethanol is a compound of interest associated with food safety and meat contamination. Polyvinylphenol (PVPh) based on linear solvation energy relationship (LSER) has been selected as an ethanol sensitive polymer. The incorporation of carbon black (CB) in the polymer matrix reduces the resistance of the composite to make it a potent resistive ethanol sensor. The percolation threshold of the CB in the PVPh matrix is determined for optimum dispersion of conducting filler to maximize the sensor response. Optical microscopy was done to visually inspect the dispersion of the filler at varied concentration of CB in the polymer matrix. The solution-processed composite was dip coated on interdigitated electrodes by computer controlled dip-coater and the number of layers was deposited based on the overall resistance of the coated film in the kilo-ohms (KO) range. Surface morphological characterizations of the sensor were conducted using AFM, SEM and profilometry. The ethanol gas sensing characterization was done for three different concentrations (1000ppm, 2500ppm and 5000ppm) with repeatability study for each concentration. The minimum detection limit of the developed sensor was found to be 233 ppm.

Evaluation of Nanostructured Novel Sensing Material for Food Contamination Applications

Food safety is a critical need to ensure public safety. Application of olfactory sensing techniques for identifying the volatile metabolites produced by microbial pathogens is a potential method for detection of food contamination. A multidisciplinary effort using parallel sensing techniques is underway at North Dakota State University to detect Salmonella contamination in packaged meat. One of the focuses of the group is to develop and evaluate novel sensor/detector that will have high sensitivity and specificity for specific indicator compounds (trace levels). In this regard, zinc oxide semi-conductor material was selected as a novel sensor material to develop sensors. The objective of the present study was to fabricate and evaluate nanoparticulate ZnO sensor for the detection of lower concentration of ethanol and acetic acid at low operating temperature. The characterization of electrodes using atomic force microscopy, scanning electron microscopy and x-ray diffraction revealed the formation of nanoparticulate structures with (100), (101) and (002) orientation of zinc oxide. The preliminary results on gas sensitivity of ZnO indicated that the electrode displays a response to acetic acid, while they did not display any sensitivity to ethanol. When sensitivity of ZnO electrodes to 50 ppm and 100 ppm acetic acid were measured at 50oC, the electrodes displayed unstable responses. It is believed that high operating temperature may be required to improve electrode sensitivity to gases.