Developing a Modern Greenhouse Scientific Research Facility-A Case Study

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

Nettle Cultivation Practices-From Open Field to Modern Hydroponics: A Case Study of Specialized Metabolites

Conventional agricultural production faces numerous challenges due to the pronounced effects of climate change, particularly global warming, and drought more than ever before in history, with the primary concern being to produce adequate yields and high-quality, nutritious plant material. Likewise, people are increasingly looking for new sources of food and are becoming aware of the importance of a varied diet and its connection to health. In this sense, stinging nettle (Urtica dioica L.) stands out as a valuable species that is neglected as a food source, as it has a significant content of specialized metabolites, and thus has an extremely high potential for use both nutritionally and pharmacologically, but is still traditionally collected from natural habitats, so it can be of questionable quality and undefined chemical composition. Therefore, sustainable agricultural practices are increasingly shifting to modern hydroponic cultivation methods in greenhouses. The advantage lies in the easier management and control of a number of factors during cultivation (air temperature and relative humidity, balanced and rational fertilization, minimization of nitrate uptake, etc.), ensuring better conditions for the growth and development of nettle according to its needs. The aim of this review is to give an overview of the technology of stinging nettle cultivation in the field and to show the possibilities of cultivation with modern hydroponic techniques to obtain a final product of consistent and uniform quality, high content of specialized metabolites and significant nutritional value. Research on this topic is still sparse but will certainly increase in the future. Therefore, this review provides all the necessary data for such future studies.

A Novel Software Architecture Solution with a Focus on Long-Term IoT Device Security Support

This paper presents a solution for upgrading a previous device model to an Industry 4.0 smart device, with the goal of maintaining high compatibility. A novel IoT architecture is presented that satisfies the characteristics of a smart device. We analysed existing IoT architectures and proposed a new architecture to achieve long-term security and usability. To ensure long-term security, we eliminated the possibility of device configuration outside the immediate vicinity of the device with a dedicated protocol. The security concepts of the existing architectures were also analysed and further modified. To improve compatibility with previous device models, we propose a new method to collect data from sensors by introducing a multithreaded microcontroller. We propose additional software components to ensure factory programming, maintenance, and cloud Big Data analysis. Based on our experiments, we adapted the algorithm to increase the accuracy of the temperature and flow sensors by using a temperature calibration device and known flow cycles. Measurement results are presented to confirm the successful upgrade. We designed a hardware architecture to ensure compatibility with previous and future device models. Issues with previous sensors encountered during the upgrade were discussed and resolved. A novel software architecture based on security for long-term IoT devices is proposed.