Phenomics Approach for Identification and Management of Plant Disease

Abstract

Phenomics research into plants allows for the non-invasive tracking of development, efficiency, and composition.The missing link between the phenome and the genome is connecting phenotypes to their underlying genetic causes.Plant phenotypic plasticity is affected by the complex interactions between the DNA, the environment, and the management of the plant.The relationship between plants and pathogens, as well as the susceptibility of plants to illness, can be investigated by using phonemics.New opportunities exist thanks to sensing technologies for detecting specific phenotypic reactions during plant-pathogen interaction, allowing for faster selection of genetic material resistant to specific pathogens or strains and greater insight into the physiological mechanisms linking pathogen infection and host disease symptoms.Changes in plant diseases that have not yet shown apparent symptoms can also be detected using phonemics.In plant diseases, digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging are all used.Magnetic resonance, soft x-ray imaging, ultrasound, and volatile chemical detection are briefly described as examples of less common techniques.It is challenging to generate representative and reliably labelled training data at this size due to the observation of only mixed spectra of plant and fungal components.For this purpose, clear spectra are required.Contaminants on a surface can be detected at an early stage using infrared light.The temperature sensitivity and real-time detection capabilities of thermal imaging make it useful.There have been significant advances in high-throughput, low-cost analysis of genetic data and in non invasive phenotyping.We hope that our work may hasten the introduction of automated, non-destructive methods for high-throughput phenotyping of plant-pathogen interactions.

Plant phenotyping relevance

植物病害に対するセンシング・画像計測・非破壊ハイスループット表現型解析を主題とする方法論的レビューであり、フェノタイピング手法が中心です。

Code and data availability

This is a narrative review article on phenomics approaches for plant disease identification. It describes general methods (RGB imaging, hyperspectral/thermal imaging, deep learning architectures) and cites prior work (e.g., Mohanty et al. 2016), but contains no paper-specific phenotype datasets, images, code, models,或s

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