The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12341

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review

Recent advancements and challenges of Internet of Things in smart agriculture: A survey

Food supply chain fraud can arise in terms of the integrity of the food item, the processes used to produce that food item and/or the people employed and the data that accompanies the food item. Emergent food fraud themes include characterization of food fraud, drivers of supply chain fraud, traceability systems and mechanisms for deterrence. Options for action at global, supply chain and organizational levels are the ongoing development of data centralization systems especially ensuring that distinct databases can be coordinated to add value through collective data analysis, and secondly ensuring there are appropriate deterrence mechanisms in place so that food fraud mitigation moves from a stance of fraud detection to one of fraud prevention.

J.M. Soon

Papers

Food fraud: policy and food chain