Aman Muhammad

Advancements in agricultural technologies have increasingly emphasized technical innovations aimed at improving the predictability and reliability of agricultural outputs. These aspects encompass developments in agricultural machinery, automation technologies, biotechnology, and controlled environment farming systems. This article focuses on Remote Sensing (RS)-based approaches applied to agricultural yield estimation for both crops and plants. RS technologies offer enhanced precision and scalability, making them particularly effective for large-scale agricultural monitoring and analysis. A systematic classification of RS-based methodologies employed for crop yield estimation is presented in this study. These methodologies are categorized into: (i) Sensor-Based approaches, (ii) Platform-Based approaches, (iii) Analytical and Modeling-based methods, and (iv) Machine Learning (ML)-driven models. Based on findings reported across multiple studies, it is observed that Deep Learning (DL)-based architectures consistently achieve superior performance across key evaluation metrics, including accuracy, precision, recall, and F1-score. This performance advantage stems from their capacity to learn hierarchical representations, capture complex non-linear relationships, scale efficiently with large datasets, and reduce reliance on manual feature engineering. Following this classification, our article presents a comprehensive discussion of the limitations associated with these methodologies. These challenges are organized into four major categories: (i) Environmental, (ii) Algorithmic, (iii) Hardware and Operational, and (iv) Wireless Sensor Networks (WSNs) related limitations. The adopted classification framework helps readers identify and address the key challenges associated with effective yield estimation in crops and plants. Moreover, the article concludes by outlining several future research directions intended to support and guide both early-career and experienced researchers in this domain.

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems.

Underwater Acoustic Communication (UWA) is an emerging wireless technology that enables reliable and long range data transmission in challenging underwater environments. To support high data rates and mitigate Inter Symbol interference (ISI) caused by multipath propagation, Orthogonal Frequency Division Multiplexing (OFDM) is widely adopted in UWA systems. High Peak-to-Average Power Ratio (PAPR) continues to be a significant constraint in OFDM based UWA systems, as it causes nonlinear distortion in power-constrained acoustic transmitters and results in spectrum regrowth. This study presents a data driven strategy for PAPR reduction using a Long Short Term Memory-Autoencoder (LSTM-AE) as well as noise reduction from time-domain OFDM signals using the Local Mean Decomposition technique (LMD). The LSTM-AE is designed to learn the basic components of the OFDM waveform and rebuild a low PAPR variant without sacrificing signal integrity. Simulation results indicate that the suggested strategy significantly decreases PAPR while maintaining Bit Error Rate (BER) performance across different Signal-to-Noise Ratio (SNR) levels. Moreover, Complementary Cumulative Distribution Function (CCDF) analysis demonstrates substantial improvement relative to conventional PAPR reduction methods, including Tone Reservation (TR), Partial Transmit Sequence (PTS), Active Constellation Extension (ACE) and Fully Connected Neural Network (FCNN). Compared to the original OFDM signal, the LSTM-AE approach reduced PAPR by 9.4 dB, reaching 4.8 dB. It also maintain better energy efficiency than ACE, FCNN, TR, and PTS at all SNR levels. The suggested LSTM-AE method is a feasible low complexity alternative for improving the efficiency and resilience of UWA OFDM systems.

Integrated sensing and communication (ISAC) has emerged as a key component of modern communication technologies that are driven by rapid advancements in wireless sensor networks (WSNs). The increasing adoption of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) is transforming various walks of life by enabling a diverse range of functional applications. As automation and connectivity continue to evolve, ISAC plays a crucial role in ensuring seamless communication and real-time data sensing, establishing itself as a fundamental pillar of contemporary technological ecosystems. Notably, ISAC offers several advantages over systems that rely on separate transceivers and receivers, including reduced costs, enhanced spectral efficiency, and improved energy efficiency. This review article provides a comprehensive overview of the core radar topologies employed in ISAC communication systems and examines the corresponding localization algorithms that align with these specific radar configurations. Furthermore, the study analyzes key parameters that can directly influence the performance of the localization estimation process within ISAC systems. Once a communication system is deployed, an important consideration is its energy consumption. To address this, the review highlights the relationship between energy usage and the types of antennas employed. Furthermore, we examine the emerging trends and techniques in ISAC systems, focusing on their role in optimizing and enhancing various communication applications such as channel estimation. We conclude this article by outlining limitations and some open challenges for future research directions in the localization of nodes in ISAC systems, providing insights into potential advancements.

Agriculture stands as a foundational element of life, closely linked to the progress and development of society. Both humans and animals depend on agriculture for a wide range of essential services, such as producing oxygen and food, along with vital raw materials for clothing, medicine, and other necessities. Given agriculture’s vital role in supporting individual well-being and driving global progress, protecting and ensuring the long-term sustainability of agriculture is essential. This is crucial for securing resources and maintaining environmental balance for future generations. In this context, in our review we have examined the various factors that can interfere with the normal physiological and developmental functions of plants and crops. These factors, referred to scientifically as stressors or stress conditions, include a wide range of both biotic and abiotic challenges. In this work we have systematically addressed all the major categories of stress that plants may encounter throughout their lifecycle. Additionally, because plants tend to exhibit recognizable physiological or biochemical responses to stress, we have cataloged the associated stress indicators. These indicators were identified through various assessment techniques, including both destructive and non-destructive approaches. A significant advancement highlighted in our review is the integration of Machine Learning (ML) algorithms with non-destructive methodologies, which has substantially enhanced the accuracy, scalability, and real-time capability of plant stress detection. These ML-enhanced systems leverage high-dimensional data acquired through remote sensing modalities, such as hyperspectral imaging, thermal imaging, and chlorophyll fluorescence. These ultimately help in enabling the early identification of biotic and abiotic stress signatures. Through advanced pattern recognition, feature extraction, and predictive modeling, ML facilitates proactive anomaly detection and stress forecasting, thereby mitigating yield losses and supporting data-driven precision agriculture. This convergence represents a significant step toward intelligent, automated crop monitoring systems. Finally, we conclude the article with a concise discussion of the potential positive roles that certain stress conditions may play in enhancing plant resilience and productivity.

In today’s digital era, where information flows seamlessly and is readily available and accessible. However, these information and communication systems are highly dependent on the robustness and adaptability of communication links, which must endure the dynamic and often unpredictable changes occurring in the communication environment, especially in underwater and underground communication. As technology advances and demands for reliable data transfer grows, the resilience of these links becomes increasingly critical to ensure an uninterrupted access to information. For this we will be analyzing a new paradigm in Wireless Sensor Networks (WSNs), i.e. Magneto Inductive (MI) communication. Which has the capability to provide robustness to the communication link as well as the security of the information can also be controlled, by utilizing the directionality factor of the created magnetic flux around the MI tri-directional (MI-TD) coils. In this study we will examine MI communication variable factors, which have effects on the output of the MI channel’s response, such as frequency of applied excitation current, number of turns, radius of the communicating coils, and changing communication distance between nodes. Than we will use a linear regression machine learning (ML) model to predict the values of the aforementioned variables, which will help to predict the MI channel’s response in the form of Path loss/Bit Error Rate (BER). The model that we have created for the prediction purpose, archives an estimation accuracy of 92%.

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs.

The deployment and efficient use of wireless sensor networks (WSNs) in underwater and underground environments persists to be a difficult task. In addition, the localization of a sensor Rx node in WSNs is an important aspect for the successful communication with the aforementioned environments. To overcome the limitations of electromagnetic, acoustic, and optical communication in underwater and underground wireless sensor networks (UWSNs), magneto-inductive (MI) communication technology emerged as a promising alternative for usage in UWSNs with a wide range of applications. To make the magneto-inductive underwater wireless sensor networks (MI-UWSNs) more efficient, recently, various research studies focused on the optimization of the physical layer, MAC layer, and routing layer, but none of them has taken into account the effect of directionality. Despite the directionality issue posed by the physical nature of a magnetic field, the unique qualities of MI communication open up a gateway for several applications. The directionality issue of MI sensors is a critical challenge that must be taken into account while developing any WSN protocol or localization algorithm. This paper highlights and discusses the severity and impact of the directionality issue in designing a localization algorithm for magneto-inductive wireless sensor networks (MI-WSNs). A received signal strength indicator (RSSI)-based multilateration localization algorithm is presented in this paper, where a minimum of 2 and maximum of 10 anchor Tx nodes are used to estimate the position of the sensor Rx nodes, which are deployed randomly in a 15 m × 15 m simulation environment. This RSSI-based multilateration technique is the most suitable option that can be used to quantify the impact of directionality on the localization of a sensor Rx node.

The unavailability of safe drinking water leads to poor conditions related to mental and physical health. To quantify the quality of water, laboratories testing the water are present in major cities which assess the basic quality parameters of drinking water, e.g., total dissolved salts (TDS), ion concentration (conductivity), turbidity, and pH value as recommended by the World Health Organization (WHO). The unavailability of such testing laboratories at remote locations makes the testing of the drinking water difficult. Establishing such laboratories is a tedious job as it requires a lot of costly equipment and specially trained personnel to operate them, making them difficult to handle. To address these issues, a water quality monitoring system for remote areas was designed which is capable of measuring basic measurable qualities of salt concentration, ion concentration, turbidity, and pH value. With the utilization of such a system, the user can qualify the water present in the vicinity as safe or unsafe for drinking purposes. The results from the proposed system are evaluated based on standard testing results and it is found that our water quality monitoring system is in agreement with the standard lab results with an average error of 2.9%, 1.4%, 1.2%, and 1.2% for pH, turbidity, conductivity, and TDS, respectively.