Abstract
The goal of this study was to create a prototype of an automatically guided rice transplanter using a Pixhauk flight controller, 3DR telemetry, Raspberry Pi, Tof sensor, gyro sensor, and other GPS navigation sensors to determine the field performance of that machine. GPS was used to obtain data such as location, direction, and speed, whereas machine vision can provide a navigation line. The next position of the transplanter can be determined using feature points extracted from the navigation line. The controller area network bus complies with the actuator control command and data communication protocols. Electrical actuators control steering and transmission based on a vehicle's location in a field. The percentage of missing, buried, and floating hills was observed to be 1.6%, 1.4%, and 3.0%, respectively, as the maximum values were found in clay soil at T1. Over time, percentages of losses decreased, and the lowest percentage of losses was given. The lowest percentage of missing, buried, and floating hills was found at 0.8%, 0.2%, and 0.8%, respectively, in clay loam soil at T5. Planting efficiency improved, and losses decreased as treatment levels upgraded. Clay loam soil in T5 had the highest planting efficiency percentage (88%), whereas clay soil in T1 had the lowest (61%) proportion. The findings indicated that T4 and T5 enhanced rice output by decreasing the losses incurred during seedling transplantation. The study suggested that the developed rice transplanter might be suitable for planting rice seedlings to establish meaningful mechanization through precision agriculture technology.
Published Version
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