Yogang Singh

Computer vision and image processing techniques have had great success in the food and drink industry. These technologies are used to analyse images, convert images to greyscale, and extract high-dimensional numerical data from the images; however, when it comes to real-time grain and rice milling processes, this technology has several limitations compared to other applications. Currently, milled rice image samples are collected and separated to avoid one contacting the another during analysis. This approach is not suitable for real-time industrial implementation. However, real-time analysis can be accomplished by utilising artificial intelligence (AI) and machine learning (ML) approaches instead of traditional quality assessment methods, such as manual inspection, which are labour-intensive, time-consuming, and prone to human error. To address these challenges, this paper presents a novel approach for real-time rice morphology analysis during milling by integrating You Only Look Once version 8 (YOLOv8) instance segmentation and Oriented Bounding Box (OBB) detection models. While instance segmentation excels in detecting and classifying both touching and overlapping grains, it underperforms in precise size estimation. Conversely, the object-oriented bounding box detection model provides more accurate size measurements but struggles with touching and overlapping grains. Experiments demonstrate that the hybrid system resolves key limitations of standalone models: instance segmentation alone achieves high detection accuracy (92% mAP@0.5) but struggles with size errors (0.35 mm MAE), while OBB alone reduces the size error to 0.12 mm MAE but falters with complex grain arrangements (88% mAP@0.5). By combining these approaches, our unified pipeline achieves superior performance, improving detection precision (99.5% mAP@0.5), segmentation quality (86% mask IoU), and size estimation (0.10 mm MAE). This represents a 71% reduction in size error compared to segmentation-only models and a 6% boost in detection accuracy over OBB-only methods. This study highlights the potential of advanced deep learning techniques in enhancing the automation and optimisation of quality control in rice milling processes.

Unmanned surface vessels USVs are typically autonomous or remotely operated and are specifically designed for environmental monitoring in various aquatic environments Aquaculture requires constant monitoring and management of water quality for the health and productivity of aquaculture systems Poor water quality can lead to disease outbreaks reduced growth rates and even mass mortality of cultured species Many small aquaculture operations operate on tight budgets and in shallow water environments such as inland ponds coastal lagoons estuaries and shallow rivers particularly in developing regions This leads to the foremost manoeuvrability challenge underscoring the crucial need for agile cost effective USVs as efficient monitoring systems The paper proposes a low cost 3D printed twin hull catamaran style platform equipped with an Inertial Measurement Unit IMU and a Global Navigation Satellite System GNSS with a two layered control framework and a differential drive configuration developed using two high efficiency T200 thrusters The design utilizes the Robot Operating System ROS to create the control framework and incorporates Extended Kalman Filter EKF based sensor fusion techniques for localisation The paper evaluates the USVs autonomy through open water captive model experiments employing remote control methods to assess the vessels manoeuvrability and overall performance in shallow water conditions

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

This paper investigates the path following control problem for a unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.