Browsing by Author "Gupta GS"

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A review of semantic segmentation methods and their application in apple disease detection
(Elsevier B.V., 2025-05-26) Keshavarzi M; Mesarich C; Bailey D; Johnson M; Gupta GS
Semantic segmentation, with pixel-wise classification, enables the precise identification of different parts of plants, as well as the diseases that occur on them, in agricultural images. Apples, as one of the most important fruit crops worldwide, are susceptible to various diseases, causing decreased crop quality and increased crop loss. To prevent disease progression and ensure prompt treatment, semantic segmentation acts as an effective method in the context of apple disease detection. This review provides a comprehensive analysis of semantic segmentation methods applied in apple disease detection, ranging from traditional approaches to state-of-the-art techniques. By systematically examining the entire pipeline, from dataset preparation to the segmentation and evaluation stages, this work not only synthesises existing knowledge but also reviews applied solutions and highlights remaining research gaps to enhance segmentation performance. Additionally, it offers a forward-looking perspective by proposing future research directions. Overall, this review aims to advance plant disease detection through semantic segmentation, with a particular emphasis on apples.
Placement optimization of multiple UAVs for energy-efficient maximal user coverage
(Elsevier B.V., 2025-10-09) Zhang C; Gui X; Gupta GS; Hasan SF
This paper proposes a deterministic Global Optimization Algorithm (GOA) for UAV-assisted communications, developed as an enhancement to the benchmark Two-Stage Optimization Algorithm (TSOA). The algorithm simultaneously addresses the dual objectives of maximizing ground user (GU) coverage and minimizing total power consumption in multiple UAV systems. Unlike existing literature, which predominantly relies on heuristic approaches, GOA provides a more precise and systematic solution to achieve optimal performance. Comprehensive simulations demonstrate that GOA achieves a 3.68 % increase in coverage count versus SOA under clustered GU distributions while delivering energy savings approximately 2.47 % (uniform) and 2.6 % (clustered) relative to the TSOA benchmark. Crucially, these efficiency gains are realized while maintaining superior GU coverage maximization versus all benchmarked methods. Both numerical results and visual analyses conclusively validate the proposed algorithm's outperformance of existing benchmarks. ©2025 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Visible Light Positioning-Based Robot Localization and Navigation
(MDPI (Basel, Switzerland), 2024-01-01) Chew M-T; Alam F; Noble FK; Legg M; Gupta GS; Pak JM
Visible light positioning or VLP has been identified as a promising technique for accurate indoor localization utilizing pre-existing lighting infrastructure. Robot navigation is one of the many potential applications of VLP. Recent literature shows a small number of works on robots being controlled by fusing location information acquired via VLP that uses a rolling shutter effect camera as a receiver with other sensor data. This paper, in contrast, reports on the experimental performance of a cartesian robot that was controlled solely by a VLP system using a cheap photodiode-based receiver rigidly attached to the robot’s end-effector. The receiver’s position was computed using an inverse-Lambertian function for ranging followed by multi-lateration. We developed two novel methods to leverage the VLP as an online navigation system to control the robot. The position acquired from the VLP was used by the algorithms to determine the direction the robot needed to move. The developed algorithms guided the end-effector to move from a starting point to target/destination point(s) in a discrete manner, determined by a pre-determined step size. Our experiments consisted of the robot autonomously repeating straight line-, square- and butterfly-shaped paths multiple times. The results show median errors of 27.16 mm and 26.05 mm and 90 percentile errors of 37.04 mm and 47.48 mm, respectively, for the two methods.