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Corn kernel classification from few training samples

Artificial Intelligence in Agriculture 2023年第3期9卷 89-99页

作者： Patricia L.Suárez Henry O.Velesaca dario carpio Angel D.Sappa ESPOL Polytechnic University CIDISCampus Gustavo GalindoGuayaquil 09-01-5863Ecuador Computer Vision Center Universitat Autònoma de Barcelona08193-BellaterraBarcelonaSpain

This article presents an efficient approach to classify a set of corn kernels in contact,which may contain good,or defective kernels along with *** proposed approach consists of two stages,the first one is a next-generation segmentation network,trained by using a set of synthesized images that is applied to divide the given image into a set of individual *** ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories(ie good,defective,and impurities).The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data *** the classification stage,the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated *** results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are ***,the segmentation and classification approach proposed can be easily adapted for use with other cereal types.

关键词： Corn kernel classification Computer vision approaches Quality inspection Food grain identification Machine vision Instance segmentation Synthesized dataset generation

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Study of the Action Mode of Wickerhamomyces anomalus against Colletotrichum gloeosporioides

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Journal of Agricultural Science and Technology(B) 2016年第5期6卷 341-349页

作者： Luis Femando Zepeda-Giraud dario Rafael Olicon-Hemandez Cinthya Martinez-Lopez Guadalupe Guerra-Sanchez Laboratorio de Bioqulmica y Biotecnologla de Hongos Bioquimica Microbiana Departamento de Microbiologia Escuela Nacionalde Ciencias Biologicas Instituto Politecnico National Carpio y Plan de Ayala S/N. Col. Casco de Santo Tomas Ciudad de Mexico CDMX CP 11340 Mexico

Colletotrichum gloeosporioides is the causal agent of anthracnose disease in fruits and vegetables, representing a global problem. The use of biocontrol agents has proved effective against fungal diseases in a wide variety of products. In this work, the antifungal activity of Wickerhamomyces anomalus against C. gloeosporioides isolated from contaminated avocados was evaluated. The antagonism and volatile compound inhibition were measured on Petri dishes. In the mixed cultures, the mycelia damage was observed by scanning electron microscope （SEM）. Chitinase and glucanase production by the antagonism was quantified by the reducing sugars method, and biofilm formation was evaluated with 1% crystal violet. The yeast W. anomalus could reduce the growth of C. gloeosporioides up to 65% by direct antagonism and 10% by volatile compounds. The antagonist did not allow the conidia germination and mycelia growth in any of the tested formulations. SEM showed mycelial damage caused by W. anomalus. The antagonist showed adhesion to the mycelium by a polysaccharide biofilm. The presence of mycelium stimulated the hydrolytic enzyme production with the maximal activity of 21.4 U/mg for chitinases at 24 h and 10 U/mg for glucanases at 60 h. These results showed that W. anomalus used together different mechanisms to express its antifungal activity against C. gloeosporioides. This study might be the first report for this phytopathogen isolated from avocado fruits, which could represent an opportunity to establish biocontrol of diseases for this agricultural product.

关键词： Colletotrichum gloeosporioides Wickerhamomyces anomalus biocontrol avocado

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