Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio

A multi-stage recurrent neural network better describes decision-related activity in dorsal premotor cortex

We studied how a network of recurrently connected artificial units solve a visual perceptual decision-making task. The goal of this task is to discriminate the dominant color of a central static checkerboard and report the decision with an arm movement. This task has been used to study neural activity in the dorsal premotor (PMd) cortex. When a single recurrent neural network (RNN) was trained to perform the task, the activity of artificial units in the RNN differed from neural recordings in PMd, suggesting that inputs to PMd differed from inputs to the RNN. We expanded our architecture and examined how a multi-stage RNN performed the task. In the multi-stage RNN, the last stage exhibited similarities with PMd by representing direction information but not color information. We then investigated how the representation of color and direction information evolve across RNN stages. Together, our results are a demonstration of the importance of incorporating architectural constraints into RNN models. These constraints can improve the ability of RNNs to model neural activity in association areas.https://doi.org/10.32470/CCN.2019.1123-0Accepted manuscrip

Variation of seed characteristic from natural and artificial selection in the genus Papaver

Papaveraceae is a mediterranean plant family with an important scientific, commercial and ethnobotanical interest. Includes wild species, which are distributed throughout the Spanish territory, as Papaver rhoeas, P. dubium, P. bracteatum, P. hybridum and the wild type of P. somniferum, which are representatives of the natural selection. The species with the greatest commercial interest is P. somniferum, whose management through improvement lines reflects the most profitable plant characteristics obtained by artificial selection: fast and vigorous growth, multiple flowers and big size capsules (1), traits that increase the production of morphine, codeine and thebaine among others opiates derived from the capsule leachate. Occasionally, some breeding lines of P. somniferum shown wild features, such as capsule dehiscence or extended germination time. Papaver becomes, thereby, an interesting plant genus to analyze the differential evolution experienced through natural and artificial selection. In this regard, previous studies reported that seed surface patter can be used in taxonomic classification of Papaveraceae family members (2). The main goal of this project was to characterize several seed traits variation among wild and cultivated samples. Seed size and shape was characterized using images analysis from macro-photography and the software ImageJ. Seed color, defined in the L, a*, b* color space, was recorded with an automatic colorimeter. Electron scanning microscopy (SEM) micrographs were used to study seed surface crosslinked patterns. Finally, confocal scanning microscopy allowed a preliminary approach to the internal seed tissue structure. The results shown that wild species seeds have deeper color than P. somniferum breeding lines but even among those it is possible to distinguish at least four main differentiated groups by color. Likewise, even if all breeded P. somniferum samples had larger seeds than wild species, probably as the results of artificial selection, there were clear variation among them. We discuss if the variations in these seed characteristics was the unintended result of the artificial trait breeding selection with agronomic interest. References * (1) Referencia general aquí * (2) Referencia sobre caracteres de la superficie de la semilla como indicadores taxonómicos Funding Authors thanks funding support from Alcaliber S.A. (OTRI-UMA-8.06/5.03.4280) and the University of Málaga to EMC, and Spanish Ministry of Enconomy and EU grant for AJMA (RYC-2011-08839).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Discriminations of Color and Pattern on Artificial Flowers by Male and Female Bumble Bees, \u3ci\u3eBombus Impatiens\u3c/i\u3e (Hymenoptera: Apidae)

This study examined the performance of male bumble bees (Bombus impatiens) in color and pattern discriminations and compared it to that of female bees. Bees were trained to forage from rewarding (S+) and unrewarding (S-) artificial flowers which differed in color (blue vs yellow) or pattern (e.g., concen­tric vs radial). Learning of the discrimination by the bees was then assessed by examining choice proportions of different flower types while none of the flowers offered reward. Color discriminations were made with 98% accuracy by the males, and the choice proportion was no different for females. Pattern discriminations were very poor or nonexistent for males but significantly better for females, especially in one of three pattern discriminations (radial vs concentric patterns)

Knockout of the US29 Gene of Human Cytomegalovirus Using BAC Recombineering

Color poster with text, diagrams, images, and charts.The purpose of this study was to determine the function of the US29 gene in Human Cytomegalovirus (HCMV) by knocking it out using Bacterial Artificial Chromosome (BAC) technology.University of Wisconsin--Eau Claire Office of Research and Sponsored Programs

Artificial color in food

Thesis (M.A.)--University of Kansas, Chemistry, 1916. ; Includes bibliographical references

April 2017 (Issue 1) : Food Color Additives

Natural Food Colors Artificial Food Colors Foods with Artificial Food Colors Reading Food Labels for Color Identification Effect of Food Colors on Young Children Food Color Adulteration Situation in Pakistanhttps://ecommons.aku.edu/nutrition_update/1016/thumbnail.jp

Color Detection Using Artificial Retina

The purpose of this project is to develop artificial retina that can be applied in robotics and other color detection needs. Project implementation is using simple electronic components such as LEDs, LDRs, a microcontroller and an LCD display