Search

This review paper addresses the importance of increasing agrobiodiversity to cope with climate change and, at the same time, providing a sufficient amount of healthy food. This is in agreement with the messages from ecology and medicine indicating the advantages of biodiversity in general and agrobiodiversity in particular for the planet and for our health. Plant breeding is considered to be one of the causes of the decline in agrobiodiversity, and therefore, this paper illustrates alternatives to the commonly used approach based on centralized selection. The first alternative is decentralized participatory breeding, which adapts crops to both different agronomic environments and client preferences, representing an "option by context" model of research. The second alternative is evolutionary breeding, which is a more dynamic strategy than participatory plant breeding because it merges the advantages of decentralization with the ability of dynamic mixtures and evolutionary populations to cope with biotic and abiotic stresses and evolve, thus adapting to climate change and to the associated changes in the spectrum of pests. A crop capable of evolving as the environment around it evolves appears to be the most ideal way of responding to climate change and increasing agricultural biodiversity. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]