The identification of plant species is fundamental for the effective study and management of biodiversity. In a manual identification process, different characteristics of plants are measured as identification keys which are examined sequentially and adaptively to identify plant species. However, the manual process is laborious and time-consuming. Recently, technological development has called for more efficient methods to meet species’ identification requirements, such as developing digital-image-processing and pattern-recognition techniques. Despite several existing studies, there are still challenges in automating the identification of plant species accurately. This study proposed designing and developing an automated real-time plant species identification system of medicinal plants found across the Borneo region. The system is composed of a computer vision system that is used for training and testing a deep learning model, a knowledge base that acts as a dynamic database for storing plant images, together with auxiliary data, and a front-end mobile application as a user interface to the identification and feedback system. For the plant species identification task, an EfficientNet-B1-based deep learning model was adapted and trained/tested on a combined public and private plant species dataset. The proposed model achieved 87% and 84% Top-1 accuracies on a test set for the private and public datasets, respectively, which is more than a 10% accuracy improvement compared to the baseline model. During real-time system testing on the actual samples, using our mobile application, the accuracy slightly dropped to 78.5% (Top-1) and 82.6% (Top-5), which may be related to training data and testing conditions variability. A unique feature of the study is the provision of crowdsourcing feedback and geo-mapping of the species in the Borneo region, with the help of the mobile application. Nevertheless, the proposed system showed a promising direction toward real-time plant species identification system.
Recent development of Advance Driver Assistance System (ADAS) has seen various advancement in object detection for vehicle vision system, particularly on the detection of other vehicles, pedestrians, road lane and signage. While these detections can provide assistant to avoid road accidents, they still lack to include road condition factors that also contributed to road accidents in Malaysia. This paper proposes a detection of the road peculiarities such as pothole and road bumps to act as additional safety feature in ADAS. With the breakthrough of deep learning in solving image recognition problems, this work takes advantage of Single Shot Detector (SSD)-MobileNetV2 as the detection algorithm, implemented on the real-time. The training images for potholes and road bumps taken from the Malaysia roads are fed into the detection model, and then the pre-trained weights are fine-tuned over the training process. The results show that the detection algorithm can predicts the potholes and road bumps, while exhibit the detection accuracy and confidence limitation due to the variety of shape and pattern of potholes and road bumps. Testing the detection algorithm with NVIDIA Jetson Nano yielded about 20 frames per second (fps), suitable for real-time applications.
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