Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality - Sudki, Julia Marconato; Fonseca de Oliveira, Gustavo Roberto; de Medeiros, André Dantas; Mastrangelo, Thiago; Arthur, Valter; Amaral da Silva, Edvaldo Aparecido; Mastrangelo, Clíssia Barboza (Frontiers in Plant Science, 2023)
How can the sanitary quality of peanut seed marketed worldwide be improved? 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. The research hypothesis is that 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). The spectral parameters of infected and healthy seeds were analyzed noninvasively. Multispectral markers of peanut seed health quality were found. Texture (Percent Run), brightness (CIELab L*) and reflectance (490 nm) were highly effective in discriminating pathogenic fungi in peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status. Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.