In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

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The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm−1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

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Ocular imaging has developed rapidly and plays a critical role in clinical care and ocular disease management. Development of image processing technologies pertinent to ocular diseases has paved the way for automated diagnostic systems including detection techniques using deep learning (DL) approaches. The prevalence of an abnormal tissue layer in the conjunctiva, known as pterygium eye disease, is increasing due to lack of awareness. Despite the non-cancerous/benign nature of pterygium, a clinical diagnosis from an ophthalmologist is still required to prevent the pterygium tissues from extending into the pupil, which would result in blurred vision. However, current diagnostic methods are mostly dependent on human expertise. Automated detection can potentially serve as an assistive method to reduce diagnosis time by applying a DL approach. Considering the lack of comprehensive research work on pterygium detection using DL, we propose a new architecture consisting of an improved CNN-based trained network named VggNet16-wbn that is derived from VggNet16, a pre-trained CNN algorithm. This paper presents an overview of the DL as a core approach to the transfer learning (TL) concept, as well as current efforts towards automated ocular detection approaches. A new architecture of a CNN-based trained network was proposed based on a network assessment from six CNN pre-trained networks to detect pterygium. This work consists of two main modules, namely, data acquisition and DCNN classification. The proposed trained network, VggNet16-wbn, shows the best performance with 99.22% accuracy, 98.45% sensitivity, and a perfect score on specificity and area under the curve metrics. This work has high potential for creating a pterygium screening system that can be used as a baseline for fully automated detection using a DL approach.