The era of big data and high-throughput genomic technology has enabled scientists to have a clear view of plant genomic profiles. However, it has also led to a massive need for computational tools and strategies to interpret this data. In this scenario of huge data inflow, machine learning (ML) approaches are emerging to be the most promising for analysing heterogeneous and unstructured biological datasets. Extending its application to healthcare and agriculture, ML approaches are being useful for microRNA (miRNA) screening as well. Identification of miRNAs is a crucial step towards understanding post-transcriptional gene regulation and miRNA-related pathology. The use of ML tools is becoming indispensable in analysing such data and identifying species-specific, non-conserved miRNA. However, these techniques have their own benefits and lacunas. In this review, we will discuss the current scenario and pitfalls of ML-based tools for plant miRNA identification and provide some insights into the important features, the need for deep learning models and direction in which studies are needed.

大数据时代和高通量基因组技术时代已使科学家能够清晰了解植物基因组概况。但是，这也导致了对解释此数据的计算工具和策略的大量需求。在海量数据流入的情况下，机器学习（ML）方法正在成为分析异质和非结构化生物数据集的最有希望的方法。ML方法将其应用扩展到医疗保健和农业领域，也可用于microRNA（miRNA）筛选。miRNA的鉴定是迈向了解转录后基因调控和miRNA相关病理的关键步骤。在分析此类数据和鉴定物种特异性的非保守miRNA时，使用ML工具变得必不可少。但是，这些技术有其自身的优势和不足。