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

Design a biomimetic disc using geometric features of the claws

This study presents a numerical investigation regarding the stress distribution on the new designed disc harrow using the ANSYS software. A conventional disc, notched disc, and a biomimetic design inspired by the claw of the leatherwing were analyzed in two conventional plowed and unplowed soils and three tillage depths (4, 7 and 10 cm). Stress analysis for all treatments showed that the highest stress was imposed at the disc-stem junction. Meanwhile the highest deformation occurred at the lowest and the most external part of the discs (land line). The results obtained in this study indicated that the maximum stress exerted from tilling soil to discs increases linearly with tillage depth in both plowed and unplowed soils. Given these results, the maximum stress also at the disc-stem junction changed linearly with tillage depth for all of the three geometric shapes. For the conventional examined harrow in unplowed soil at a depth of 10 cm, the highest maximum stress was 484 MPa and the maximum deformation was 1.84 mm. Using the new geometry for discs in plowed soil, the highest maximum stress and the maximum disc deformation were obtained equal to 130 MPa and 0.92 mm at the same tillage condition, respectively. For all treatments in plowed or unplowed soil, the lowest stress occurred with the biomimetic harrow. The soil- disc interaction stresses exerted on the notched harrow was lower than the conventional disc

Construction and development of an automatic sprayer for greenhouse

Abstract: This paper presents design and construction of an autonomous robot for using in greenhouse condition.  The robot designed to prevent human hazards involved in spraying potentially toxic chemicals in the confined space of a hot and steamy glasshouse.  In order to navigate the robot, hot water piping rails along the rows were used as a method of guidance for autonomous robot.  The robot is able to force and back along the hot water piping rails of rows in greenhouse avoiding the expensive and complicated navigation systems.  Power was transmitted from two DC motors to two driving wheels through a gearbox and shaft system.  The AVR microcontroller controls all of the inputs and outputs of the system.  To program the micro used from BASCOM-AVR version 1.11.9.8 and for circuit simulating used from PROTEUS 7 professional.  The obtained Results indicated that the robot is capable to cover more than 90% of surface which needed to spray.   Keywords: Autonomous robot, human hazards, design, spraying, greenhouse, AVR Microcontrolle

A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties

Producción CientíficaSince different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP

Cutaneous leishmaniasis in Iran: A review of epidemiological aspects, with emphasis on molecular findings

Leishmania parasites can cause zoonotic cutaneous leishmaniasis (CL) by circulating between humans, rodents, and sandflies in Iran. In this study, published data were collected from scientific sources such as Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google scholar, and SID. Keywords searched in the articles, theses, and abstracts from 1983 to 2021 were cutaneous leishmaniasis, epidemiology, reservoir, vector, climatic factors, identification, and Iran. This review revealed that CL was prevalent in the west of Iran, while the center and south of Iran were also involved in recent years. The lack of facilities in suburban regions was an aggravating factor in the human community. Some parts of southern Iran were prominent foci of CL due the presence of potential rodent hosts in these regions. Rhombomys opimus, Meriones lybicus, and Tatera indica were well-documented species for hosting the Leishmania species in Iran. Moreover, R. opimus has been found with a coinfection of Leishmania major and L. turanica from the northeast and center of Iran. Mashhad, Kerman, Yazd, and sometimes Shiraz and Tehran foci were distinct areas for L. tropica. Molecular identifications using genomic diagnosis of kDNA and ITS1 fragments of the parasite indicated that there is heterogeneity in leishmaniasis in different parts of the country. Although cutaneous leishmaniasis has been a predicament for the health system, it is relatively under control in Iran

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.This research was funded by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. This project has also been supported by the 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-JP

Characterization of liquid spray impact onto walls and films

The present work is concerned with the characterization of liquid spray impact onto walls and films. Empirical studies on spray impact rely almost solely on the phase Doppler instrument for obtaining quantitative data about drop size and velocity distributions. The thesis begins therefore with a careful examination of applying the phase Doppler instrument to new-wall measurements beneath a spray. This includes consideration of the influence of the measurement volume height above the rigid wall, the input laser power and the spatial location of the detection volume on the measured characteristics of the impinging and secondary spray. This knowledge is then used for all subsequent measurements made within the framework of this investigation. After a qualitative and quantitative characterization of the resulting secondary spray and the accumulated wall film, a set of empirical models is presented for prediction of the characteristics of the secondary spray generated due to a liquid spray impact onto a rigid wall. In the models, characterization of the secondary spray has been formulated in terms of correlations for the velocity and trajectory of secondary droplets and the mass and number ratio of the secondary spray. The novel aspect of the model is that the correlations are based on the mean statistics over many events in the spray and not on the outcome of single drop impact experiments. Another interesting feature of the experiments is the rather large range of oblique impact angles captured, due to the different drop trajectories exiting from the spray. A phase Doppler instrument has been used to measure drop size and two components of velocity directly above the target. A high-speed CCD camera has been used to measure the average film thickness formed due to spray impact. In a second step, a theoretical model to predict the average film thickness formed due to a liquid spray impinging onto a flat and rigid wall is presented. This model takes into account the characteristics of the impinging spray, e.g. flux density of impacting droplets, hydrodynamic pressure of the impinging spray and viscosity of the impacting liquid droplets. It considers the mass and momentum balance of the film, including viscosity effect and neglecting the Laplace pressure. A second simplified model for predicting the average film thickness as a function of mean Reynolds number and flux density of the impacting droplets and the average drop diameter is presented based on dimensional analysis. Both theoretical derivations for the average film thickness show good agreement with most of the measurements. This thesis also provides an experimental comparison of the splashing phenomenon for single drops and for drops in a spray, followed by a derived theoretical model. Such a comparison can be very valuable for future modelling of spray impact. The last section of this thesis presents an experimental study for different aspects of liquid spray impact onto a deep liquid layer under well controlled experimental conditions; deformation of the air-liquid film interface due to the hydrodynamic pressure exerted by the impacting drops, the generation of a secondary spray, and the air bubble entrainment into the liquid film. A high-speed CCD camera has been used to measure the deformation of the air-liquid film interface and the distribution of the air bubbles inside the deep liquid film. Two different configurations of a phase Doppler instrument have been used to measure drop size and two components of velocity directly above the film as well as the size and two components of velocity of the air bubbles inside the deep pool

Design, construction and evaluation of a seed pod husker and testing with soybean and mung bean

In this research, a new rubbing threshing machine was designed, constructed and evaluated based on the equations, tables and standards of machine components design, and its performance was analyzed and compared theoretical and practical. It is made with electric motor, inverter, husking roller, mechanical jack, belt and etc. This built-in machine has a width of 300 mm, working height of 100 mm, speed between 110 to 210 rpm and a maximum power of 2 hp. The practical test was carried out on two products (soybean and mung bean). Moisture content varies between 12% and 17%, speed and the distance between drum and concave vary in three levels (110, 170 and 210 rpm) and (7, 8, 9 mm) for soybean and (6, 7, 8 mm) for mung bean, respectively. The experiments were carried out in a completely randomized design with factorial experiment in three replications. The results showed that the capacity of the machine for soybean and mung bean was 28.506 and 29.079 kg/hr, respectively. The best efficiency of the machine was 94.72%, which is related to the mung bean and was obtained at a speed of 170 rpm and a distance of 7 mm. The best separation and loss efficiency were 93% and 1.66%, which was achieved at a speed of 170 rpm and a distance of 7 mm for mung bean and soybean, respectively. The best germination efficiency was 95.53%, which was achieved at a distance of 7 mm and a speed of 110 rpm during mung bean test