Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.

ABSTRACT: The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of pean

SUBMITTER: Sudki JM 

PROVIDER: S-EPMC9992408 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature