Travis Esau

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.

Detecting Weeds in Wild Blueberry Field Based on Color Images

Weeds are the major fruit yield limiting factor in wild blueberry fields. Wild blueberry producers apply herbicide uniformly without considering the significant bare spots and weed patches within field. The objective of this research was to develop automated system for long leaf weeds detection and mapping for site-specific application of agrochemicals to reduce cost and environmental pollution. The developed machine vision system includes two components: image grabbing and image processing. In image processing component, every color image was grabbed and downloaded to computer in real-time. It was binarized based on pixel green value by a threshold, and then edge detection method was used to produce an edge image. Last, peak value in Hough space of the edge image was calculated, which represented the main feature of long leaf weeds. The experiment was conducted at a 0.4 ha area of the selected wild blueberry field in central Nova Scotia. The results indicated that the automated machine vision system has a potential to detect weeds in real-time for site-specific application of agrochemicals in wild blueberry fields.

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 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.

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.

Impact of Biosolid Application on Percolated Water Quality

Application of municipal biosolids as a fertilizer source on agricultural land not only provides essential nutrients to the plants but also improves the physical and chemical properties of soil. An experiment was conducted at the Wild Blueberry Research Institute, Debert, NS to investigate the agronomic and environmental impact of N-Viro (biosolids) application on wild blueberry fields under rainfed and irrigated conditions. There were four treatments (i.e. commercial fertilizer, N-Viro, commercial fertilizer with irrigation, and N-Viro with irrigation) and each treatment was replicated four times. Suction lysimeters were installed at 20 cm and 40 cm depths in each plot and samples of leachate percolated through the soil profile were collected after each irrigation and or rainfall throughout the experiment. Samples were analyzed for the nutrients and heavy metals. The results will be presented in this paper.