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A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant

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Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
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Seeds For Innovation, Calle Regaliz, 6, 04007 Almería, Spain
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SOMDATA, Carrer d’Alvaro de Bazan, 10, 46010 València, Spain
*
Author to whom correspondence should be addressed.
Biology 2020, 9(9), 272; https://doi.org/10.3390/biology9090272
Received: 24 July 2020 / Revised: 28 August 2020 / Accepted: 2 September 2020 / Published: 5 September 2020
(This article belongs to the Section Plant Science)
The development of double haploids (DHs) is a straightforward path for obtaining pure lines but has multiple bottlenecks. Among them is the determination of the optimal stage of pollen induction for androgenesis. In this work, we developed Microscan, a deep learning-based system for the detection and recognition of the stages of pollen development. In a first experiment, the algorithm was developed adapting the RetinaNet predictive model using microspores of different eggplant accessions as samples. A mean average precision of 86.30% was obtained. In a second experiment, the anther range to be cultivated in vitro was determined in three eggplant genotypes by applying the Microscan system. Subsequently, they were cultivated following two different androgenesis protocols (Cb and E6). The response was only observed in the anther size range predicted by Microscan, obtaining the best results with the E6 protocol. The plants obtained were characterized by flow cytometry and with the Single Primer Enrichment Technology high-throughput genotyping platform, obtaining a high rate of confirmed haploid and double haploid plants. Microscan has been revealed as a tool for the high-throughput efficient analysis of microspore samples, as it has been exemplified in eggplant by providing an increase in the yield of DHs production. View Full-Text
Keywords: androgenesis; anther culture; microspores; RetinaNet; Solanum melongena androgenesis; anther culture; microspores; RetinaNet; Solanum melongena
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García-Fortea, E.; García-Pérez, A.; Gimeno-Páez, E.; Sánchez-Gimeno, A.; Vilanova, S.; Prohens, J.; Pastor-Calle, D. A Deep Learning-Based System (Microscan) for the Identification of Pollen Development Stages and Its Application to Obtaining Doubled Haploid Lines in Eggplant. Biology 2020, 9, 272.

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