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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]

Global drylands, covering about 41% of Earth's surface and inhabited by 38% of the world's population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind. [ABSTRACT FROM AUTHOR]