Advanced Spectroscopic, Imaging, and Nanotechnology Tools for Diagnosing Fungal Diseases in Fruits.

Abstract

Fruits are a critical component of the human diet, as they provide essential dietary nutrients that play an important role in the functioning of the human body and maintaining health. It is well-known that consuming fruits has various benefits, including the prevention of chronic diseases, cancer, and cardiovascular disorders. Thus, wider availability and maintaining the quality of fruits are highly required. Around 25% of global crop losses reported annually are attributed to disease and pest infestations, as per the Food and Agriculture Organization. Fungal pathogens are a major cause of post-harvest diseases, which significantly affect production and lead to economic losses. To address this, disease diagnosis at an early stage is crucial to enable timely monitoring, implementation of prevention techniques, and minimizing storage-related losses. Various methods are available for early pathogen detection; spectroscopic and imaging techniques have been widely applied as they offer cost-effectiveness, potential for real-time analysis, and a non-destructive nature of analysis. When integrated with advanced decision-support tools, these instrumental techniques can enable rapid and accurate detection of fungal diseases in fruits. In recent years, nanotechnology has emerged as a promising approach, with a wide range of nanoparticles being utilized to develop nanobiosensors for various applications. This review also highlights recent advancements in the use of nanomaterials and nanoparticle-based sensing systems for the detection of pathogens, providing an overview of their potential role in improving post-harvest disease diagnostics.

Plant phenotyping relevance

果実の真菌病という植物器官の病状態を対象に、分光・画像診断ツールを中心としてレビューしており、病害状態の取得・検出手法が主題である。

Code and data availability

This is a review article on spectroscopic, imaging, and nanotechnology tools for fungal disease detection in fruits. The authors state no datasets were generated, and no paper-specific phenotype datasets, images, code, or models with public availability are described. The Statista URL is a generic production-statistics

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