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.
Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.
A elevada susceptibilidade da soja ao mofo-branco tem impactado negativamente a sua produtividade. Atualmente métodos de controle biológico como o uso de Trichoderma spp. têm proporcionado alternativas ao controle químico, com menor impacto ambiental e favorecimento do desenvolvimento vegetal. Objetivou-se avaliar os efeitos de T. harzianum sobre a emergência de sementes de soja inoculadas com Sclerotinia sclerotiorum; além de verificar a interação entre os fungos e com a soja, por meio da microscopia eletrônica de varredura (MEV ABSTRACTThe high susceptibility of soybean to white mold has negatively impacted its productivity. Currently, biological control methods, such as the use of Trichoderma spp., have provided alternatives to chemical control, as they promote less environmental impact and favor the plant growth. The aim of this study was to evaluate the effects of T. harzianum on the emergence of soybean seeds inoculated with Sclerotinia sclerotiorum, besides identifying the interaction between fungi and soybeans by means of scanning electron microscopy (SEM). Soybean seeds were subjected to the following treatments: 1. Without S. sclerotiorum, in PDA+Mannitol; 2. With S. sclerotiorum, in PDA+Mannitol; 3. With Ecotrich ® WP and without S. sclerotiorum, in PDA+Mannitol; 4. Silva, F.F.; Castro, E.M.; Moreira, S.I.; Ferreira, T.C.; Lima, A.E.; Alves, E. Emergence and ultrastructural analysis of soybean seedlings inoculated with Sclerotinia sclerotiorum under the effect of Trichoderma harzianum application. Summa Phytopathologica, v.43, n.1, p.41-45, 2017. With S. sclerotiorum and Ecotrich ® WP, in PDA+Mannitol. The emergence test was conducted on trays with sterile sand at 25 °C for 8 days. Then, the vegetative organs of emerged seedlings were sectioned to evaluate, by scanning electron microscopy (SEM), T. harzianum potential to parasitize and inhibit S. sclerotiorum. In fact, the white mold is capable of colonizing and deteriorating all soybean seeds. T. harzianum is effective in colonizing the root system of soybeans but does not contribute to emergence, compared to control. Based on the ultrastructural analysis, mycoparasitism of T. harzianum and S. Sclerotiorum was evidenced, but the control of the causal agent of white mold was not as effective as shown in other studies.
The sterile insect technique (SIT) has been widely used to suppress several fruit fly species. In southern Brazil, millions of sterile flies of the South American fruit fly, Anastrepha fraterculus Wiedemann (Dipetra: Tephritidae), will be produced in a mass‐rearing facility called MOSCASUL to suppress wild populations from commercial apple orchards. In spite of standard rearing conditions, the quality of pupal batches can be inconsistent due to various factors. The quantification of poor quality material (e.g. empty pupae, dead pupae or larvae) is necessary to track down rearing issues, and pupal samples must be taken randomly and evaluated individually. To speed up the inspection of pupal samples by replacing the manual testing with the mechanized one, this study assessed a multispectral imaging (MSI) system to distinguish the variations in quality of A. fraterculus pupae and to quantify the variations based on reflectance patterns. Image acquisition and analyses were performed by the VideometerLab4 system on 7‐d‐old pupae by using 19 wavelengths ranging from 375 to 970 nm. The image representing the near infrared wavelength of 880 nm clearly distinguished among high‐quality pupae and the other four classes (i.e. low‐quality pupae, empty pupae, dead pupae and larvae). The blind validation test indicated that the MSI system can classify the fruit fly pupae with high accuracy. Therefore, MSI‐based classification of A. fraterculus pupae can be used for future pupal quality assessments of fruit flies in mass‐rearing facilities.
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