BB2_Projects

Semi-autonomous Agricultural Robot   Farming is crucial for life-sustaining and green life expansion. In agricultural fields, heavy machinery tools are used for plowing and performing agricultural tasks. However, this leads to high fuel expenses and multiple carbonic compound emissions. Supervised farming employs a number of employees to perform farming tasks and machinery controls. In this paper, a semi-autonomous robot is designed to endure the tasks set for employees and reduce carbon pollution. The robot is intended to navigate within the plots without supervision while performing seed sowing and fertilizing simultaneously.   1.011                   Types of crop agricultural robot system Recently, there are several types of agricultural robots with a set of definition and classification methods. The wide range of researches carried out and technological studies targeting its speci...

 In this blog, we cover pothole detection designing using the Matlab computer vision toolbox. Potholes are road defects that are caused by depression in road surfaces during wet seasons and heavy traffics. Fiji roads are susceptible to potholes due to the weather pattern and heavy traffics.  One of the most persistent issues is the prevalence of potholes, which can cause accidents and damage to vehicles.  The pothole detection model is compromised with strategies that enveloped a complete functional model. However, as there are various existing pre-trained models that have already existed, we will try to train and perform some comparisons among these state-of-arts. Yolov2 Yolov3 Fast RCNN Faster RCNN Methods followed for training a Pothole Detection model The strategies taken to train a Pothole Detection involve other sub-strategies that are necessary to take before feeding data into a neural-based model. Fig 1: Task Execution Flow Image Labeling Image Labelling is t...