With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learning-enabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

Vehicle control in autonomous traffic flow is often handled using the best decision-making reinforcement learning methods. However, unexpected critical situations make the collisions more severe and, consequently, the chain collisions. In this work, we first review the leading causes of chain collisions and their subsequent chain events, which might provide an indication of how to prevent and mitigate the crash severity of chain collisions. Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency of existing methods in driving security. To address this, A reward function is being developed to deal with the challenge of multiple vehicle collision avoidance. A perception network structure based on formation and on actor-critic methodologies is employed to enhance the decision-making process. Finally, in the safety efficiency analysis phase, we investigated the safety efficiency performance of the agent vehicle in both single-agent and multi-agent autonomous driving environments. Three state-of-the-art contemporary actor-critic algorithms are used to create an extensive simulation in Unity3D. Moreover, to demonstrate the accuracy of the safety efficiency analysis, multiple training runs of the neural networks in respect of training performance, speed of training, success rate, and stability of rewards with a trade-off between exploitation and exploration during training are presented. Two aspects (single-agent and multi-agent) have assessed the efficiency of algorithms. Every aspect has been analyzed regarding the traffic flows: (1) the controlling efficiency of unexpected traffic situations by the sudden slowdown, (2) abrupt lane change, and (3) smoothly reaching the destination. All the findings of the analysis are intended to shed insight on the benefits of a greater, more reliable autonomous traffic set-up for academics and policymakers, and also to pave the way for the actual carry-out of a driver-less traffic world.

Digital media faces growing challenges related to unauthorized access, manipulation, and copyright infringement, necessitating robust solutions for authentication and copyright protection. This research introduces a novel dual watermarking scheme based on Discrete Wavelet Transform (DWT) to address these issues. The primary goal is to offer robust copyright protection by embedding a watermark containing ownership and copyright information in the spatial domain using DWT, serving as a digital signature for identifying and tracing rightful ownership. Additionally, the frequency domain embedding improves the watermark's resilience to common attacks. The scheme also incorporates a fragile watermark for content authentication, embedded in the least significant bit to detect tampering. By comparing the fragile watermark, the content's integrity and authenticity are verified. Experimental results confirm the scheme's effectiveness, showing high imperceptibility and robust performance in protecting against unauthorized alterations while preserving visual quality. The proposed scheme achieved robustness performance with NC value of 0.98 under JPEG compression, the scheme also has ability to ensure the tamper detection with accuracy of 0.996 under tampering rate of 0.84.