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Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies. [ABSTRACT FROM AUTHOR]

The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%. [ABSTRACT FROM AUTHOR]

Sustainable Farming requires up-to-date advice on crop diseases, patterns, and adequate prevention actions to face developing circumstances. Currently, in developing countries like Egypt, farmers' access to such information is extremely limited due to the agriculture support being either not available, inconsistent, or unreliable. The presented Cloud-based Complaints Management and Decision Support System for Sustainable Farming in Egypt, named as AgroSupportAnalytics, aims to resolve the problem of both the lack of support and advice for farmers, and the inconsistencies in doing so by current manual approach provided by agricultural experts. Key contribution is the development of an automated complaint management and decision support strategy, on the basis of extensive research on requirement analysis tailored for Egypt. The solution is grounded on the application of knowledge discovery and analysis on agricultural data and farmers' complaints, deployed on a Cloud platform, to provide farming stakeholders in Egypt with timely and suitable support. This paper presents the overall system architectural framework along with the information and storage services, which have been based on the requirements specifications phases of the project along with the historical data sets of past 10 year of farmers complaints and enquiries in Egypt. [ABSTRACT FROM AUTHOR]

The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the management of finite resources such as land, the need to reduce livestock contribution to greenhouse gases, and the need to manage inherent complex, highly contextual, and repetitive day-to-day livestock management (LsM) routines are some examples of challenges to overcome in livestock production. The Internet of Things (IoT)'s usefulness in other vertical industries (OVI) shows that its role will be significant in LsM. This work uses the systematic review methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to guide a review of existing literature on IoT in OVI. The goal is to identify the IoT's ecosystem, architecture, and its technicalities--present status, opportunities, and expected future trends--regarding its role in LsM. Among identified IoT roles in LsM, the authors found that data will be its main contributor. The traditional approach of reactive data processing will give way to the proactive approach of augmented analytics to provide insights about animal processes. This will undoubtedly free LsM from the drudgery of repetitive tasks with opportunities for improved productivity. [ABSTRACT FROM AUTHOR]

• Provide quick sorting of patients using Alzheimer Disease Intensity Number (ADIN). • Enable estimation of the progression of disease and within a patient's context. • Achieve sufficient expressiveness in understanding patients' disease information. • Knowing the context suggest related treatment(s) or an estimated life expectancy. Modern neuroscience imaging technologies considerably affect diagnostic and prognostic accuracy and facilitate progress towards the cure of brain diseases. The benefits largely depend on the practicalities by which the large-scale imaging and clinical data can be integrated, examined and understood. In EU neuGRID4You (N4U) project, many datasets were generated from research centres and hospitals. In order to perform effective analyses, these datasets and their metadata along with a number of pre-computed parameters are stored in a big data repository. This paper focuses on the patient identification using big data and Fuzzy Logic, which has been achieved through fuzzy processing where a reference number called Alzheimers Disease Identification Number (ADIN) is calculated. It has enabled patients sorting for a particular intensity of Alzheimers disease, short-term estimation of the progression of that disease and context of individual patients with respect to other patients such as appropriate treatment, estimated life expectancy etc. The generated rules define the necessary knowledge base for the inference engine to generate output sets and an aggregate membership function of each rule is formed. Using this function, a most representative value of the total output set is obtained which represents the disease intensity. The implemented system and its evaluation are based on realistic datasets, demonstrators and making use of real-life neuroscience case studies. The presented results of four selected case studies show that this approach have provided sufficient expressiveness in understanding patients' disease information. Finally, a discussion and conclusions are presented on the opportunities offered by the calculation of ADIN to manage Alzheimers disease along with potential future extensions or applications of this work. [ABSTRACT FROM AUTHOR]