ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development

Abstract

The current era of rapid climate change necessitates greater emphasis on wild, often underutilized yet sturdy, edible plants that are capable of growing in harsh arid lands. When compared to more popular crops like rice, these are often of traditional significance and more region-specific; but needing less chemical fertilizers, pesticides and irrigation water, they can not only provide food and nutrition in a sustainable manner but also medicinally valuable compounds (nutraceuticals) to target various communicable and non-communicable diseases. These bioactive metabolites could also serve as markers for in-process quality control of herbal formulations and as metabolic biomarkers. Of late, a few of the common food crops across the world have benefited from the use of technological interventions, employing various Internet of Things (IoT) devices and sensors to collect data on the farm and conduct agro-food specific analytics. Machine Learning (ML) and deep learning (DL) have found application in numerous facets of agriculture, particularly in tasks such as yield prediction, disease detection, weed detection, crop recognition, and assessing crop quality at pre-harvest, harvest, and post-harvest stages. ML technology also has shown potential to be effectively employed at various stages of bioactives discovery, encompassing target identification, compound screening, lead discovery, as well as pre-clinical and clinical development phases. However, the usage of these modern technologies has been less explored in the desert plants of the world. The current article reviews a few available examples and highlights the potential of employing ML and DL technologies in edible plants of the world, with a focus on sustainable desert flora, for achievement of multidisciplinary objectives, that is, agro-food production, food safety and bioactives discovery.