Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea

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.

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

“…The SVM- l algorithm showed to be efficient in discriminating high-and low-vigor soybean seeds compared to LDA models 34 . However, the LDA approach has achieved the highest training speed, with high potential to classify the quality of beans 35 and oat 19 seeds from multispectral sources.…”

The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research

“…For MSI, seeds require very little preparation beyond the preparation required for the application or experiment at hand. For example, if the aim was to see if it is possible to identify the presence of particular fungi on seeds, the first step might be to work with sterilized seeds before inoculating them with the fungus/fungi of interest [26][27][28][29][30]. Similarly, it may be necessary to artificially age seeds for different lengths of time, to explore the use of MSI for predicting whether seeds are viable or dead, or parameters related to vigor (e.g., El Masry et al [31]).…”

“…Seeds located too close to each other may touch or even overlap and cause occlusion leading to poorer segmentation and adding noise to the extracted features. To prevent seeds from moving, when placing them in the Petri dish, they may be fixed with double-sided tape [29,32] or placed on an insert with small recesses inside the Petri dish. When placing the seeds, it is important to consider which side is most relevant for the application and thus should be facing the imaging sensor.…”

“…or Alternaria alternate [26], and recently by detection of Drechslera avenae and Helminthosporium avenae in black oat/oats seeds (Avena strigosa) [32], Fusarium pallidoroseum, Rhizoctonia solani, and Aspergillus sp. in cowpea [29].…”

Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool

“…The difference between these modes is the number and width of the recorded spectral bands. In multispectral imaging 3–10 bands are used [ 4 ]. In hyperspectral imaging hundreds or thousands of correspondingly more narrow bands are employed [ 5 , 6 , 7 , 8 ].…”