Using the Convolution Neural Network (YOLOv8) with Cartesian Robot for Automatic Disease Detection and Watering
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Abstract
Currently, smart farm has influence to more agriculture. Due to production costs have decreased and productivity has increased. However, the study found that diseases occurring in plants still require human observation. A convolutional neural network (YOLOv8) with a cartesian robot for automatic disease detection and watering is proposed in this research. Which species of cactus are used as experimental model in this research. When the soil moisture falls below the target level, the robot is moved to the target location to watering the plants and collect images for disease detection using artificial intelligence. In case a disease is detected, the robot displays an alert on the LINE application and moves the injector to target position to spray disease eradication. During the research process, the robot moves to each target location and collects images of cactuses. After that, artificial intelligence is used for disease detection and measuring the usage time. Subsequently, the disease detection results from the before and after images are validated. The results indicated that the average prediction time was approximately 0.57 seconds and the accuracy was 90 percentage.
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ลิขสิทธ์ ของมหาวิทยาลัยเทคโนโลยีราชมงคลพระนครReferences
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