A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera

Abstract

India ranks among the top ten nations in the world for grape production. Fungal pathogens inflict damage to crop plants in turn making cultivators bear huge economical losses. With an output of 1.21 million tons (about 2% of 57.40 million tons produced globally). 1.2% of the nation’s total fruit cropland is covered by grapes. But due to fungal diseases the effect of the yield produced ranges from 5-80% depending on the severity of diseases which will affect the yield of grape vineyard. In precision agriculture, new sensing technologies and artificial intelligence could be used to automatically identify grapevine and disease pest symptoms. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Timely effective and precise evaluation of grape diseases is admitted as a critical step in the field management. In this paper, we are explaining about different optical sensing methods applied for RGB, Multispectral and Thermal cameras. Section-wise we will be describing environmental set up for image-aquation, data-preprocessing, different modelling methods, evaluation matrix, result, and reviewer’s comment.

Plant phenotyping relevance

植物の真菌病徴をRGB・マルチスペクトル・熱画像で検出する方法を体系的に扱うレビューであり、植物状態の取得・推定手法が中心です。

Code and data availability

This is a review article summarizing prior studies (Gutierrez et al., Ji et al., Wang et al., Fernandez et al., Bendel et al., Fahrentrapp et al., etc.). The authors present no original phenotyping measurements, datasets, images, code, or models of their own, and no public availability statements or URLs for any assets

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