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

IF 4.8 Q1 AGRICULTURE, MULTIDISCIPLINARY Journal of Agriculture and Food Research Pub Date : 2024-08-08 DOI:10.1016/j.jafr.2024.101350
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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.

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基于 ML 的可持续农业食品生产及其他技术:利用(半)干旱地貌开发生物活性产品
当今时代气候变化迅速,因此有必要更加重视野生的、通常未得到充分利用但却能在严酷的干旱地区生长的坚固可食用植物。与水稻等更受欢迎的作物相比,这些植物往往具有传统意义,而且更具有地区特异性;但它们对化肥、杀虫剂和灌溉水的需求较少,不仅能以可持续的方式提供食物和营养,还能提供有药用价值的化合物(营养保健品),以防治各种传染性和非传染性疾病。这些生物活性代谢物还可作为草药制剂过程质量控制的标志物和代谢生物标志物。近来,全球一些常见的粮食作物已受益于技术干预,采用各种物联网(IoT)设备和传感器收集农场数据并进行农业食品特定分析。机器学习(ML)和深度学习(DL)已在农业的许多方面得到应用,特别是在产量预测、疾病检测、杂草检测、作物识别以及在收获前、收获和收获后阶段评估作物质量等任务中。在生物活性物质发现的各个阶段,包括目标识别、化合物筛选、先导物发现以及临床前和临床开发阶段,ML 技术也显示出有效应用的潜力。然而,这些现代技术在世界沙漠植物中的应用还较少。本文回顾了几个可用的例子,并强调了在全球可食用植物中采用 ML 和 DL 技术的潜力,重点关注可持续沙漠植物,以实现农业食品生产、食品安全和生物活性发现等多学科目标。
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来源期刊
CiteScore
5.40
自引率
2.60%
发文量
193
审稿时长
69 days
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