Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield

A precise microalgae farming for CO2 sequestration: A critical review and perspectives

Microalgae are great candidates for CO2 sequestration and sustainable production of food, feed, fuels and biochemicals. Light intensity, temperature, carbon supply, and cell physiological state are key factors of photosynthesis, and efficient phototrophic production of microalgal biomass occurs only when all these factors are in their optimal range simultaneously. However, this synergistic state is often not achievable due to the ever-changing environmental factors such as sunlight and temperature, which results in serious waste of sunlight energy and other resources, ultimately leading to high production costs. Most control strategies developed thus far in the bioengineering field actually aim to improve heterotrophic processes, but phototrophic processes face a completely different problem. Hence, an alternative control strategy needs to be developed, and precise microalgal cultivation is a promising strategy in which the production resources are precisely supplied according to the dynamic changes in key factors such as sunlight and temperature. In this work, the development and recent progress of precise microalgal phototrophic cultivation are reviewed. The key environmental and cultivation factors and their dynamic effects on microalgal cultivation are analyzed, including microalgal growth, cultivation costs and energy inputs. Future research for the development of more precise microalgae farming is discussed. This study provides new insight into developing cost-effective and efficient microalgae farming for CO2 sequestration.

An Anonymous Authentication and Key Update Mechanism for IoT Devices Based on EnOcean Protocol

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily accessible by malicious attackers, attackers can analyze users' habits based on the intercepted information. This paper analyzed the security of the EnOcean protocol using a formal analysis method based on the colored Petri net (CPN) theory and the Dolev-Yao attacker model and found that the protocol did not anonymize the device identity information and did not have a communication key update mechanism, so an attacker could easily initiate a key compromise impersonation attack (KCIA) after breaking the pre-shared communication key. To address the above security issues, this paper proposed an EnOcean-A protocol with higher security based on the EnOcean protocol. The EnOcean-A protocol introduced a trusted third-party server to send communication keys to communication devices because devices must obtain different communication keys from the trusted third-party server each time they communicated. Thus, this protocol could resist a KCIA and achieve forward security. Meanwhile, the device identity information was anonymized using a homomorphic hash function in the EnOcean-A protocol, and the dynamic update mechanism of the device identity information was added so that an attacker could not obtain the real identity information of the device. Finally, the formal analysis of the EnOcean-A protocol showed that the new protocol could resist a KCIA and ensure the anonymity and untraceability of the communication device, which had higher security compared with the EnOcean protocol.

An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.

Labour Implications on Agricultural Production in Romania

Throughout this paper, the theoretical concepts from the above areas were combined with the analysis and interpretation of statistical data from the same areas of interest, resulting in a detailed analysis of how the agricultural labour force influences the yields of the five most important crops in the Romanian agricultural sector, namely, wheat, maize, sunflower, rapeseed and soybean. The analysis was carried out within the eight NUTS-listed development regions. A bibliometric analysis of the importance of the academic environment for agricultural labour force research was previously carried out using VOSviewer software. The content of this document aims to determine the impact that the agricultural labour force has on the productivity of the five main crops cultivated in Romania over large areas during the period 2015–2019, where, although the population employed in agriculture has decreased, the yields of these crops have increased due to the technological development process started in agriculture. As of 2019, only 9% of the total Romanian population is represented by the population employed in agriculture and 39.41% and 61.37% of the total area of the country are represented by arable/agricultural area, respectively.

The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory

This paper aims to examine the factors influencing the willingness of Bangladeshi farmers to adopt and pay for the Internet of Things (IoT) in the agricultural sector by applying the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). To this end, the study employed a quantitative research methodology and obtained data from 345 farmers from the northern districts of Bangladesh. Using a cross-sectional survey design and convenience sampling method, a study of premium fruit growers was undertaken to assess IoT use in agriculture, and the primary survey data were analyzed using the Structural Equation Modeling (SEM) approach via AMOS 26. The study confirmed that effort expectancy, performance expectancy, facilitating condition, hedonic motivation, government support, price value, personal innovativeness, and trust influence the willingness of Bangladeshi farmers to adopt the IoT. Additionally, predictors such as trust and willingness to adopt were observed to influence the willingness to pay for the IoT, while the construct ‘performance expectancy’ produced no effect. The study also revealed that the willingness to adopt moderates the association between performance expectancy, price value, and willingness to pay for the IoT. This research has novel implications because it investigates the behavior of rural customers with respect to innovation adoption, which in this case is the IoT in agriculture. It outlines precise reasons for the willing adoption of the IoT in agriculture, which will, in turn, assist marketers of IoT technology in the design of appropriate marketing strategies to increase acceptance in rural areas. Using the proposed model that incorporates farmers’ willingness to pay, this empirical study takes the first step in examining whether farmers in a developing economy such as Bangladesh will adopt and pay for the IoT.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Insights into Efficient Irrigation of Urban Landscapes: Analysis Using Remote Sensing, Parcel Data, Water Use, and Tiered Rates

To understand how landscape irrigation can be better managed, we selected two urban irrigation systems in northern Utah, USA, and performed a statistical analysis of relationships among water use, irrigated area, plant health (based on the Normalized Difference Vegetation Index), and water rate structures across thousands of parcels. Our approach combined remote sensing with 4-band imagery and on-site measurements from water meters. We present five key findings that can lead to more efficient irrigation practices. First, tiered water rates result in less water use when compared to flat water rates for comparable plant health. Second, plant health does not strictly increase with water application but has an optimum point beyond which further watering is not beneficial. Third, many water users irrigate beyond this optimum point, suggesting that there is water conservation potential without loss of aesthetics. Fourth, irrigation is not the only contributor to plant health, and other factors need more attention in research and in water conservation programs. Fifth, smaller irrigated areas correlate with higher water application rates, an observation that may inform future land use decisions. These findings are especially pertinent in responding to the current drought in the western United States.