INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS

Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industryplays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developedin many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring andautomation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users.Â

Impacts of soil and water pollution on food safety and health risks in China

Environmental pollution and food safety are two of the most important issues of our time. Soil and water pollution, in particular, have historically impacted on food safety which represents an important threat to human health. Nowhere has that situation been more complex and challenging than in China, where a combination of pollution and an increasing food safety risk have affected a large part of the population. Water scarcity, pesticide over-application, and chemical pollutants are considered to be the most important factors impacting on food safety in China. Inadequate quantity and quality of surface water resources in China have led to the long-term use of waste-water irrigation to fulfill the water requirements for agricultural production. In some regions this has caused serious agricultural land and food pollution, especially for heavy metals. It is important, therefore, that issues threatening food safety such as combined pesticide residues and heavy metal pollution are addressed to reduce risks to human health. The increasing negative effects on food safety from water and soil pollution have put more people at risk of carcinogenic diseases, potentially contributing to ‘cancer villages’ which appear to correlate strongly with the main food producing areas. Currently in China, food safety policies are not integrated with soil and water pollution management policies. Here, a comprehensive map of both soil and water pollution threats to food safety in China is presented and integrated policies addressing soil and water pollution for achieving food safety are suggested to provide a holistic approach

Biopiracy <i>versus </i>one-world medicine – from colonial relicts to global collaborative concepts

Background: Practices of biopiracy to use genetic resources and indigenous knowledge by Western companies without benefit-sharing of those, who generated the traditional knowledge, can be understood as form of neocolonialism.Hypothesis: : The One-World Medicine concept attempts to merge the best of traditional medicine from developing countries and conventional Western medicine for the sake of patients around the globe.Study design: Based on literature searches in several databases, a concept paper has been written. Legislative initiatives of the United Nations culminated in the Nagoya protocol aim to protect traditional knowledge and regulate benefit-sharing with indigenous communities. The European community adopted the Nagoya protocol, and the corresponding regulations will be implemented into national legislation among the member states. Despite pleasing progress, infrastructural problems of the health care systems in developing countries still remain. Current approaches to secure primary health care offer only fragmentary solutions at best. Conventional medicine from industrialized countries cannot be afforded by the impoverished population in the Third World. Confronted with exploding costs, even health systems in Western countries are endangered to burst. Complementary and alternative medicine (CAM) is popular among the general public in industrialized countries, although the efficacy is not sufficiently proven according to the standards of evidence-based medicine. CAM is often available without prescription as over-the-counter products with non-calculated risks concerning erroneous self-medication and safety/toxicity issues. The concept of integrative medicine attempts to combine holistic CAM approaches with evidence-based principles of conventional medicine.Conclusion: To realize the concept of One-World Medicine, a number of standards have to be set to assure safety, efficacy and applicability of traditional medicine, e.g. sustainable production and quality control of herbal products, performance of placebo-controlled, double-blind, randomized clinical trials, phytovigilance, as well as education of health professionals and patients

Impact of trade liberalization on agriculture in the near East and North Africa:

Trade liberalization Africa, Agricultural trade., Economic development Africa, Sub-Saharan., Sustainable agriculture Africa, Sub-Saharan, Agricultural marketing, Agricultural policy Africa, Sub-Saharan, Agriculture Economic aspects Africa,

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

Artificial intelligence for agricultural supply chain risk management: Constraints and potentials

Supply chains of staple crops, in developed and developing regions, are vulnerable to an array of disturbances and disruptions. These include biotic, abiotic and institutional risk factors. Artificial intelligence (AI) systems have the potential to mitigate some of these vulnerabilities across supply chains, and thereby improve the state of global food security. However, the particular properties of each supply chain phase, from "the farm to the fork," might suggest that some phases are more vulnerable to risks than others. Furthermore, the social circumstances and technological environment of each phase may indicate that several phases of the supply chains will be more receptive to AI adoption and deployment than others. This research paper seeks to test these assumptions to inform the integration of AI in agricultural supply chains. It employs a supply chain risk management approach (SCRM) and draws on a mix-methods research design. In the qualitative component of the research, interviews are conducted with agricultural supply chain and food security experts from the Food and Agricultural Organization of the UN (FAO), the World Bank, CGIAR, the World Food Program (WFP) and the University of Cambridge. In the quantitative component of the paper, seventy-two scientists and researchers in the domains of digital agriculture, big data in agriculture and agricultural supply chains are surveyed. The survey is used to generate assessments of the vulnerability of different phases of supply chains to biotic, abiotic and institutional risks, and the ease of AI adoption and deployment in these phases. The findings show that respondents expect the vulnerability to risks of all but one supply chain phases to increase over the next ten years. Importantly, where the integration of AI systems will be most desirable, in highly vulnerable supply chain phases in developing countries, the potential for AI integration is likely to be limited. To the best of our knowledge, the methodical examination of AI through the prism of agricultural SCRM, drawing on expert insights, has never been conducted. This paper carries out a first assessment of this kind and provides preliminary prioritizations to benefit agricultural SCRM as well as to guide further research on AI for global food security