Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN

In recent years, computer vision systems have been used in almost every field of industry. In this study, image processing algorithm has been developed by using CUDA (GPU) which is 79 times faster than CPU. We had used this accelerated algorithm in destemming process of pepper. 65 percent of total national production of pepper is produced in our cities, Kahramanmaras and Gaziantep in Turkey. Firstly, hybrid intuitionistic fuzzy algorithm edge detection has been used for preprocessing of original image and Otsu method has been used for determining automatic threshold in this algorithm. Then the multilayer perceptron artificial neural network has been used for the classification of patterns in processed images. Result of ANN test for detection direction of pepper has shown high accuracy performance in CPU-based implementation and in GPU-based implementation

Speed and position control of autonomous mobile robot on variable trajectorydepending on its curvature

513-521This paper presents design of autonomous mobile robot MBR-01 for speed and position control on variable trajectorydepending on trajectory curvature. MBR-01 can communicate reciprocally with host computer using RF data transceiver. Roaddata image is captured by CCD camera mounted on vehicle and transferred to host computer using RF data link unit. Applyingimage processing on trajectory, reference speed has been produced depending on curvature of trajectory. Reference speed isapplied to fuzzy controller unit and output is sent to vehicle by wireless transmitter unit. Received control signal by vehicle istransferred to DC motor drive system with Pulse Width Modulation techniques. Position control is realized by microprocessorbasedunits mounted on vehicle. Equipped 7 optical sensors detect trajectory deviation and wheel angle of vehicle for trackdetection and wheel angle detector unit

CUDA-Based Hybrid Intuitionistic Fuzzy Edge Detection Algorithm

Intuitionistic fuzzy edge detection algorithm has been used for the signification or characterization of images. It has been designed by experts and the algorithm provides to aim to minimize errors. However, it has a fixed value for thresholding. In this paper, a hybrid algorithm has been developed using the Otsu method which is calculated a threshold value depending on the images. To be applicable in parallel of intuitionistic fuzzy edge algorithm is pave the way for accelerating of algorithm by performing in the graphics card. Intuitionistic fuzzy logic edge detection algorithm has been tested by transferring different size images to graphics cards which has different computing capacity via Compute Unified Device Architecture (CUDA) programming environment which is manufactured by NVIDIA. Parallel model of the algorithm adapted to CUDA platform, compared to serial application running on processor, and has seen that shortened runtime at least 67 times, most 639 times

Accelerated Intuitionistic Fuzzy Edge Detection Algorithm by Using CUDA

Intuitionistic fuzzy edge detection (IFED) algorithm has been used in the signification or characterization of images. IFED algorithm has been designed by the experts and the algorithm provides to aim to minimize errors of them. To be applicable in parallel of IFED is pave the way for accelerating of algorithm by performing in the graphics card. In this study, IFED algorithm was tested by transferring different size images to graphics cards which has different computing capacity via Compute Unified Device Architecture (CUDA) programming environment which is manufactured by NVIDIA. Parallel model of the algorithm adapted to CUDA platform, compared to serial application running on processor, and has seen that shortened runtime at least 67 times, most 641 times

Field Sprayer with Application Rate Control Using Fast Response Proportional Valves under Variable Speed Conditions

In modern agriculture, which is characterised by dynamic field environments, challenges are faced in maintaining consistent application rates due to varying tractor speeds, field conditions, and certain calibration errors. Conventional control systems, which rely on slower valves, have difficulty adapting to these dynamic field conditions. By contrast, the integration of fast-acting proportional valves improves the precision and flexibility of flow rate adjustment during spraying applications. This research focused on evaluating the accuracy of spraying applications under different tractor speed conditions through field experiments and data analysis. This study involves a field sprayer with boom wings divided into right and left sections, where the flow rate of the liquid to each section is controlled by proportional valves with a 3 s full opening and closing time, dependent on speed information. Using a closed-loop control system consisting of a flow meter, proportional valve, and PLC, the valves are controlled by the PLC’s internal PID blocks. Observations reveal that as the tractor speed increases to a certain level, the system effectively adjusts the application rate close to the target value and maintains control against the changing ground speed during all field tests. The study included five different application tests, with target application rates of 100, 150, 200, 250, and 300 L ha−1, with each repeated three times, resulting in a total of 15 field tests at different ground speeds. During these tests, the data were meticulously recorded every second, covering the tractor speed, flow rate, and pressure values for both right and left boom sections, along with regulator pressure, proportional valve opening rates, and application rates. The durations for each application rate were documented alongside instances within specified periods where error boundaries of ±10% were exceeded. During the total test duration of 9734 s, the actual application rate value exceeded error boundaries during only 209 s. Within the application durations, the speed variation intervals ranged from 5.10 to 10.23 km h−1, 4.64 to 9.91 km h−1, 3.68 to 7.89 km h−1, 4.80 to 8.21 km h−1, and from 4.90 to 8.69 km h−1. The absolute percentage mean application errors were recorded as 2.81%, 2.68%, 2.28%, 2.14%, and 2.51% for respective application rates. Furthermore, statistically significant correlations (p < 0.01) were identified among the variables (speed, valve opening rate, flow rate, pressure) in both the right and left boom sections across all application rates