Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea

Carlos Henrique Queiroz Rego,Francisco Guilhien Gomes-Junior,Fabiano França-Silva,Clíssia Barboza Da Silva,André Dantas De Medeiros,Maria Heloisa Duarte De Moraes

doi:10.3390/agriculture10080361

Carlos Henrique Queiroz Rego, Francisco Guilhien Gomes-Junior + Show 4 more

Open Access

https://doi.org/10.3390/agriculture10080361

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Abstract

Recent advances in multispectral imaging-based technology have provided useful information on seed health in order to optimize the quality control process. In this study, we verified the efficiency of multispectral imaging (MSI) combined with statistical models to assess the cowpea seed health and differentiate seeds carrying different fungal species. Seeds were artificially inoculated with Fusarium pallidoroseum, Rhizoctonia solani and Aspergillus sp. Multispectral images were acquired at 19 wavelengths (365 to 970 nm) from inoculated seeds and freeze-killed ‘incubated’ seeds. Statistical models based on linear discriminant analysis (LDA) were developed using reflectance, color and texture features of the seed images. Results demonstrated that the LDA-based models were efficient in detecting and identifying different species of fungi in cowpea seeds. The model showed above 92% accuracy before incubation and 99% after incubation, indicating that the MSI technique in combination with statistical models can be a useful tool for evaluating the health status of cowpea seeds. Our findings can be a guide for the development of in-depth studies with more cultivars and fungal species, isolated and in association, for the successful application of MSI in the routine health inspection of cowpea seeds and other important legumes.

Highlights

Seeds were inoculated with three fungi species isolates (Fusarium pallidoroseum, Rhizoctonia solani and Aspergillus sp.)
The normalized canonical discriminant analysis (nCDA) method revealed a slight distinction among classes before incubation (Figure 1a) compared to seeds after incubation (Figure 1b): the intense colonization of the fungi after incubation showed greater separation between healthy and unhealthy seeds and among the different fungal species
This study sought to verify the efficiency of multispectral imaging (MSI) in recognizing different fungal species associated with cowpea seeds; it was possible to observe distinctions in the spectral signature between the different seed classes

Summary

Introduction

Walp) is a leguminous species which is of nutritional and social importance in underdeveloped regions due to the high protein content in its grains [1,2]. Despite its adaptability and rusticity, cowpea seeds are susceptible to several fungal diseases. According to Biemond et al [4], the contamination of cowpea seeds by Aspergillus flavus, Macrophomina phaseolina, Fusarium oxysporum and Penicillium sp. Contribute to a marked reduction in germination and seed weight, in addition to acting on accelerating deterioration by producing aflatoxins, limiting commercialization of its seeds and consumption of grains. The seed health is mainly monitored by detecting fungi species and their percentages present in the sample, which contributes to make decision regarding the suitability of a lot Agriculture 2020, 10, 361; doi:10.3390/agriculture10080361 www.mdpi.com/journal/agriculture According to Biemond et al [4], the contamination of cowpea seeds by Aspergillus flavus, Macrophomina phaseolina, Fusarium oxysporum and Penicillium sp. contribute to a marked reduction in germination and seed weight, in addition to acting on accelerating deterioration by producing aflatoxins, limiting commercialization of its seeds and consumption of grains.

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