ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support vector machine (SVM) and deep learning (deep neural network, DNN) are applied to predict the number of raisins and to decide whether the raisins need more segmentation. The performance of the three models is compared, and the DNN is the most accurate.

Para proveer guías acerca de la utilización y conservación de los parasitoides del defoliador forestal Orgyia ericae se inventariaron sus especies de parasitoides. Se identificaron los estados de desarrollo del lepidóptero con las distintas especies de parasitoides así como sus patrones de emergencia. Se adelantaron observaciones de emergencia desde las 7am hasta 12pm hasta que todos los adultos emergieron. Tiempo de pupación, patrones de emergencia y abundancia del parasitoide Exorista larvarum se registró en detalle. Se analizaron la abundancia y patrones de emergencia de Bracon (Habrobracon) sp. y Tetrastichus sp. Bracon (Habrobracon) sp. y E. larvarum exhibieron un solo pico de emergencia mientras que Tetrastichus sp. muestra dos picos. La emergencia diaria de Bracon (Habrobracon) sp., Tetrastichus sp. y E. larvarum fue mayor entre las 3 a 5pm, 3 a 5pm y 7 a 8am, respectivamente. La pupación de E. larvarum mostró dos picos.

To provide guidance on the utilization and conservation of the forest defoliator Orgyia ericae parasitoids, we survey its parasitoid species. We identifed the developmental stages associated with the different parasitoids, and the emergence pattern of adult parasitoids. Observations of hatching were conducted hourly from 7am to 12pm until all adults had emerged. Pupation time, emergence patterns, and the abundance of Exorista larvarum parasitoids were recorded in detail. The abundance and daily emergence patterns of Bracon (Habrobracon) sp., and Tetrastichus sp. were analyzed. Bracon (Habrobracon) sp. and E. larvarum both had only one emergence peak, whereas Tetrastichus sp. had two. The daily emergence of Bracon (Habrobracon) sp., Tetrastichus sp. and E. larvarum peaked at 3 to 5pm, 3 to 5pm and 7 to 8am, respectively. The E. larvarum pupation distribution was characterized by two peaks.