Version 1
: Received: 8 November 2023 / Approved: 8 November 2023 / Online: 9 November 2023 (07:20:00 CET)
How to cite:
Yadahalli, G.H.; Sukali, G.H.; S, A.T.; Walikar, G.A. A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera. Preprints2023, 2023110552. https://doi.org/10.20944/preprints202311.0552.v1
Yadahalli, G.H.; Sukali, G.H.; S, A.T.; Walikar, G.A. A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera. Preprints 2023, 2023110552. https://doi.org/10.20944/preprints202311.0552.v1
Yadahalli, G.H.; Sukali, G.H.; S, A.T.; Walikar, G.A. A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera. Preprints2023, 2023110552. https://doi.org/10.20944/preprints202311.0552.v1
APA Style
Yadahalli, G.H., Sukali, G.H., S, A.T., & Walikar, G.A. (2023). A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera. Preprints. https://doi.org/10.20944/preprints202311.0552.v1
Chicago/Turabian Style
Yadahalli, G.H., Aishwarya T S and Gyanappa A Walikar. 2023 "A Review on Plant Fungal Disease Detection based on RGB, Multispectral and Thermal Camera" Preprints. https://doi.org/10.20944/preprints202311.0552.v1
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.
Keywords
fuzzy logic; convolution neural network; surf features; support vector machines; FTIR spectrum; maximum temperature difference (MTD)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
