Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

The degradation of water quality has become a critical concern worldwide, necessitating innovative approaches for monitoring and predicting water quality. This paper proposes an integrated framework that combines the Internet of Things (IoT) and machine learning paradigms for comprehensive water quality analysis and prediction. The IoT-enabled framework comprises four modules: sensing, coordinator, data processing, and decision. The IoT framework is equipped with temperature, pH, turbidity, and Total Dissolved Solids (TDS) sensors to collect the data from Rohri Canal, SBA, Pakistan. The acquired data is preprocessed and then analyzed using machine learning models to predict the Water Quality Index (WQI) and Water Quality Class (WQC). With this aim, we designed a machine learning-enabled framework for water quality analysis and prediction. Preprocessing steps such as data cleaning, normalization using the Z-score technique, correlation, and splitting are performed before applying machine learning models. Regression models: LSTM (Long Short-Term Memory), SVR (Support Vector Regression), MLP (Multilayer Perceptron) and NARNet (Nonlinear Autoregressive Network) are employed to predict the WQI, and classification models: SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), Decision Trees, and Random Forest are employed to predict the WQC. Before that, the Dataset used for evaluating machine learning models is split into two subsets: Dataset 1 and Dataset 2. Dataset 1 comprises 600 values for each parameter, while Dataset 2 includes the complete set of 6000 values for each parameter. This division enables comparison and evaluation of the models’ performance. The results indicate that the MLP regression model has strong predictive performance with the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values, along with the highest R-squared (0.93), indicating accurate and precise predictions. In contrast, the SVR model demonstrates weaker performance, evidenced by higher errors and a lower R-squared (0.73). Among classification algorithms, the Random Forest achieves the highest metrics: accuracy (0.91), precision (0.93), recall (0.92), and F1-score (0.91). It is also conceived that the machine learning models perform better when applied to datasets with smaller numbers of values compared to datasets with larger numbers of values. Moreover, comparisons with existing studies reveal this study’s improved regression performance, with consistently lower errors and higher R-squared values. For classification, the Random Forest model outperforms others, with exceptional accuracy, precision, recall, and F1-score metrics.

Reliable data transfer seems a quite challenging task in Underwater Wireless Sensor Networks (UWSN) in comparison with Terrestrial Wireless Sensor Networks due to the peculiar attributes of UWSN communication. However, the reliable data transmission in UWSN is very limited. Yet, there is a way to achieve reliable data transfer metrics through the design of routing protocols by considering the exceptional features of UWSN communications. With this aim, we propose two schemes with multiple sinks-based network architecture: Anchor Nodes assisted Cluster-based Routing Protocol (ANCRP) to achieve reliable data transfer metrics and Void Handling technique in ANCRP (VH-ANCRP) to cope with the local maximum nodes. For which, the network space is divided into small cubes to form clusters. Then, each cube is assigned with an anchor node as a cluster head (CH). All cluster heads are supposed to be anchored at the centroid of a cube via a string, while source nodes are randomly distributed. In ANCRP, the source nodes are liable to send the sensed data to their designated CH. The CH transmits the sensed data to the next-hop CH and continues this procedure till the successful delivery of the data packets at the surface sinks. In VH-ANCRP, a void handling technique of making the ad-hoc CH is used by the void nodes to reconnect with the network operations. We perform extensive simulations in NS3 to validate our schemes. The simulation outcomes expel that both proposed schemes have improved the network performance when compared with the baseline schemes.

Underwater Sensor Network (UWSN) is gaining popularity among researchers due to its peculiar features. But there are so many challenges in the design of the UWSN system, and these are quite unsustainable due to the dynamic nature of water waves. Perhaps the most tedious challenge for UWSNs is how to transfer the data at the destination with a minimal energy rate. It can be accomplished by exploiting geographic and opportunistic routing schemes to send the data efficiently to the surface sinks in cooperation with relay nodes. With this aim, we introduce a new protocol for routing, named Geographic and Cooperative Opportunistic Routing Protocol (GCORP). In GCORP, the packets are routed from the source node to the surface sinks in coordination with intermediate relay nodes. In GCORP protocol, initially, multiple sinks-based network architecture is established. Then, a relay forwarding set is being determined by the source node on the basis of depth fitness factor. Finally, the best relay is determined through the weight calculation scheme from the relay forwarding set. We conduct the simulations in NS3 to validate the proposed GCORP routing protocol concerning different network metrics. The simulations conclude that the GCORP protocol shows better performance than existing approaches.

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm’s environmental parameters can help to curtail the negative impacts of the environment on chickens’ health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O₂), carbon dioxide (CO₂), carbon monoxide (CO), and ammonia (NH₃) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O₂ concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO₂ levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO₂ levels within the control sheds. The NH₃ gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).