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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 820 Artificial Neural Network and Particle Swarm Optimization in Orange Identification Mr. J. Jayakumar M.E1, V.S. Adelin Nittas 2 1Assistant professor, Department of Electronics and Communication Engineering, Annai Vailankanni College Of Engineering, Azhagappapuram, Kanniyakumari, Tamil Nadu, India. 2Final year PG student, Department of Communication Systems, Annai Vailankanni College Of Engineering, Azhagappapuram, Kanniyakumari, Tamil Nadu, India. ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract- Advancement in technology hasleadtotheuse of computer science and it’s associated technologyinagriculture and food science. Fruit industry contributes a major part in the growth of a nation. Identifyingfoodsthatlookssimilarisof major concern. And also, the rise in labour costs, shortage of efficient labourers and the need to improve the processes at the production side have all put pressure on producers and processors to demand for a time-saving, economic, consistent and non-destructive inspection method. In such scenarios, automation can reduce the costs while increasing the production efficiency. Automatic fruit grading and sorting requires the implementation of computer vision systems. Hybrid approaches that consists of artificial neural networks and meta-heuristic algorithmscanprovidesatisfyingsolutions to these problems. This can provide fast and accurate ways in classifying fruits. This paper deals with the use of artificial neural networks and Particle SwarmOptimization(ANN-PSO) in identifying orange varieties. Key Words: Artificial Neural Network (ANN), Metaheuristics, Artificial Neurons, Particle Swarm Optimization, Nature inspired metaheuristics. 1. INTRODUCTION Fruits play vital part in a balanced diet. They are said to be good sources of minerals and vitamins and for their role in preventing related deficiencies. People those who have the habit of eating fruits as part of their daily diet are generally less prone to chronic diseases. Fruitsprovidemany essential nutrients including fibre, potassium, folate and vitamin C. The nutrients in fruit are vital for maintaining the health of one’s body. Oranges are relatively hard to classify when compared with other fruit varieties. Here, we mainly consider three types of oranges namely Bam, Pavyandi and Thomson. The use of Computer Vision Systems in the field of agriculture has increased substantially in the recent years for the substantial information it provides about the nature and attributes of the produce while reducing costs and guaranteeing the maintenance of quality standards in real time. Computer vision is a newtechnologywhichacquires an image of a scene and analyses it using computers in order to control machines or to process it. It makes use of the chromatic (colour) and geometric (shape, size, texture) attributes that are present on the image to predict the outcome. The tasks involved in Computer vision are the methods to acquire, process, analyze and understand the digital images, and to extract high-dimensional data from those images so as to produce the decision in either numerical or symbolic forms. Artificial systems have been designed to act based on the science of computer vision which extracts information from images. The image data that can be used to extract the necessary information can be of many forms, such as sequence of video, multiple camera views from various angles or data from medical scanners that are usually multi- dimensional. Computer vision alongwith artificial neural networksisused to identify the type or category to which the given image of orange belongs to. 2. LITERATURE SURVEY Yudong Zhang and his associates have combined fitness- scaled chaotic artificial bee colony (FSCABC) algorithm[7] and feed forward neural network (FNN) which is a hybrid method to classify objects. The acquired images were processed for background removal and to obtain various features to create feature space. Since, the obtained feature space is large principal component analysis was performed to reduce the dimensions of the feature space. And then, these were fed to the FNN. The FSCABC algorithm was used to train the weights of the neural network. This method proved to acquire higher classification efficiency than other genetic algorithms. Yudong Zhang and his associates proposed another novel fruit-classification system, with the goal ofrecognizingfruits in a more efficient way. It employs four-step pre-processing before the extraction of various geometric and chromatic attributes. Principal component analysis was utilized in the decrease of the quantity of features. Feed-forward neural network (FNN)andBiogeography-basedoptimization(BBO) [8] had been utilized to group the fruits from the diminished features. This technique had higher productivity in both precision and calculation time.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 821 Wang S and his associates in their work proposed two novel machine-learning based classification methods. Their work involved the utilization of wavelet entropy (WE), principal component analysis (PCA). And mainly, the two machine- learning based classification methods namely feed-forward neural network (FNN) along with fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO) were employed. This required the utilization of considerably less features in expectationwhen contrasted and different techniques. Nasirahmadi and his associate Ashtiani S in their paperhave grouped fruits and classified them using Image processing combined with pattern recognition. They classified 20 varieties almonds which are of sweet and bitter taste utilizing Bag-of-Feature (BOF) model. Harris, Harrise Laplace, Hessian, Hessiane Laplace and Maximally Stable Extremely Regions (MSER) key point indicatorsalongwitha Scale Invariant Feature Transform (SIFT) descriptor were utilized. The execution of Chi-SVM classifier was found be exceptional than the k-NN and L-SVM classifiers. Sofu MM, Erb O, Kayacan MC, Cetissli B. The paper proposed real-time processing system that consequently sorts apples and assesses it's quality. The apples were arranged dependent on various characteristics, for example, colour, size and weight into different classes. It was likewise ready to recognize the apples that were influenced by scab and stain and furthermore the one's that were spoiled. The proposed framework had the capacity to sort 15 apples in a normal for each second. It had a arranging precision rate of 73– 96%. 3. ARTIFICIAL NEURAL NETWORKS Artificial neural network (ANN) has also been known as connectionist system. Normally, artificial neurons are collected into layers. It is essentiallya registeringframework that was motivated by the working of the natural neural systems of creature minds [1]. The neural network is essentially not an algorithm. It is a system for the AI calculations to chip away at to process complex information inputs [2]. They figure out how to play out the assignments while handling the issue by considering the precedents as the procedure proceeds onward with no errand explicit tenets. The process takes place without any prior knowledge. They do so by generating characteristics that identify the object at the time of processing. An artificial neural network is basically a system that consists of components called artificial neurons, which get input, change their inward state (initiation) as indicated by that input, and produce the necessary output upon the information on the input and activation. As in general, in an ANN the signs between artificial neurons are genuine numbers, and the yield of each neuronisfigured by some non-direct capacity of the entirety of its sources of info. The associations between neurons are called 'edges'. Artificial neurons and edges commonly have a weight that alters as learning continues. The weight increments or diminishes the quality of the flag at an association. The flag might contain a threshold such that the signals are sent only if the total of the flag crosses that threshold. Typically, artificial neurons are accumulated into layers. Distinctive layers may perform various types of changes on their data sources. Signs travel from the primary layer (the information/input layer), to the last layer (the yield/output layer), conceivably in the wake of navigating the layers on numerous occasions as in Figure 1. Figure 1- Information flow in ANN The network is framed by associating the yield of specific neurons to the contribution of different neurons shaping a coordinated, weighted diagram. The weights just as the functions that figure the activities can be adjusted by a procedure called learning which is administered by a learning rule. The learning rule is a standard or a calculation which changes the parameters of the neural network, all together for an offered contribution to the system to deliver a favoured yield. This learning procedure ordinarily sums to altering the weights and threshold of the factors inside the system. 3.1 Components of Artificial Neural Network Neurons A neuron that is labelled as after receiving an input (t) from antecedent neurons comprises of the accompanying segments: an activation (t) representing the state of neuron, threshold , which is usually fixed, an activation function used in the computation of the new activation at a time from (t), and the (t) which gives the following relation (t+1)= (t), (t), ,
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 822 and an output function (t)= (t) . Generally, the output function is essentially the Identity function. Connections, weights and biases The network comprises of associations (connections),every association exchanging the output of a neuron to the input of a neuron . In such a way that input neuron is the ancestor of output neuron . Every association is appointed a weight . Now and again an inclination term is added to the all out weighted aggregate of contributions to fill in as a limit to move the activation function. Propagation function The propagation registers the contribution to the inputs (t) from the outputs (t) of antecedent neurons and commonly has the structure At the point when a bias is included with the capacity, the above structure changes to , where is a bias. 4. METAHEURISTICS In software engineering and mathematical optimization, a metaheuristic is a more elevated amount methodology or heuristic intended to discover, create, or select a heuristic (partial search algorithm) that may give an adequately decent answer for an advancement issue, particularly with fragmented or flawed data or constrained calculation limit [5]. Metaheuristics test a lot of arrangements which is too vast to even think about being totally inspected. Metaheuristics in general makes a couple of presumptions about the improvement issue being illuminated, thus they might be usable for a variety of issues. Contrasted with improvement calculations and iterative techniques, metaheuristics don't ensure that an all around ideal arrangement can be found on some class of problems.[6] Many metaheuristics actualize some type of stochastic advancement, so the arrangementfoundisreliant on the arrangement of arbitrary factors generated.[5] In combinatorial optimization, via looking over a huge arrangement of possible arrangements, metaheuristics can frequently discover great arrangements with less computational exertion than enhancement calculations, iterative strategies, or basic heuristics.[6] As such, they are helpful methodologies for advancement issues. Properties These are properties that describe generallymetaheuristics: Metaheuristics are systems that direct the hunt (search) procedure. The objective is to effectively investigate the hunt space so as to discover near– ideal arrangements. Techniqueswhichcomprise metaheuristiccalculations extend from basic neighbourhood seek techniques to complex learning forms. Metaheuristic calculations are rough and more often than not non-deterministic. Metaheuristics are not issue explicit. Types of metaheuristics There are wide assortments of metaheuristics and various properties as for which to arrange them. Local search vs. global search Single-solution vs. population-based Hybridization and memetic algorithms Parallel metaheuristics Nature-inspired metaheuristics Applications used for combinatorial advancement in whichanideal arrangement is looked for over a discrete hunt space. multidimensional combinatorial issues, incorporating most structure issues in designing which experience the ill effects of the curse of dimensionality. Metaheuristics are additionally generally utilized for job shop booking and occupation determination issues. Heuristics is a path by trial and mistaketocreatesatisfactory answers for a perplexing issue in a sensibly down to earth time. The point is to discover greatdoablearrangementinan adequate timescale. Two noteworthy parts of metaheuristic calculations are: Intensification Diversification Diversification intends to produce differingarrangementsin order to investigate the hunt space on the global scale, while intensification intends to concentrate on the inquiry in a nearby district by using the data that a present decent arrangement is found in this area. The greatmixof thesetwo noteworthy parts will for the most part guarantee that the worldwide optimality.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 823 4.1 NATURE-INSPIRED METAHEURISTICS ALGORITHMS Nature roused metaheuristic calculations are notable efficient methodologies for taking care of a few hard advancement issues. These calculations are roused from explicit qualities of creatures existing in nature and natural marvels. Nature enlivened metaheuristic calculationscanbearranged into four noteworthy divisions: Evolution- Based Method Physics-Based Method Swarm-Based Method and Human-Based Method A portion of the nature-inspired algorithms are as per the following: Ant colony optimization Ant lion optimizer Artificial bee colony algorithm Bat algorithm Cat swarm optimization Cuckoo search algorithm Differential evolution Firefly algorithm Harmony search Particle swarm optimization Shuffled complex evolution Simulated annealing 4.2 PARTICLE SWARM OPTIMISATION In computational science, particleswarmoptimization(PSO) is a strategy that upgrades an issue by iterativelyattempting to improve a hopeful arrangement concerning a given proportion of value. It takes care of an issue by having a populace of candidate solutions, particles, and movingthese particles around in the pursuit space as indicated by straightforward numerical formulae over the molecule's position and velocity [4]. PSO is instated with a gathering of unpredictable particles (solutions) and then checks for optima by reviving the generations. In each cycle, every particle is refreshed by following two "best" values. The first is the best solution (fitness) it has accomplished up until this point. Thisvalue is known to be pbest. The next "best" that's taken is the best value, obtained so distant by any particle within the populace. This best esteem is a global best and called gbest. lbest is chosen as the nearby best value when a particle removes a portion of the populace neighbours. In the wake of finding the two best values, the particle refreshes it’s velocity and position using the equations (a) and (b). (a) (b) ,the velocity of the particle, the present particle (solution). , random number between (0,1). , are the learning factors. Typically = =2 The decision of PSO parameters can largelyaffectoptimizing execution. Choosing PSO parameters that yield great execution has always been the subject of much research. The PSO parameters can likewise be tuned by utilizing another overlaying analyzer, an idea known as meta- optimization, or even adjusted amid the improvement, e.g., by methods for fuzzy logic [4]. Advantages Simple to actualize. Just couple of parameters ought to be balanced. 4.3 USE OF METAHEURISTICS IN ORANGE IDENTIFICATION So as to distinguish the kinds of oranges, a fewhighlights,for example, texture, colour, shape were recognized. The quantity of highlights range to hundreds. Each object found in the pictures is spoken to by a tuple of 263 qualities, portraying texture, colour, shape and state of the comparing object. This high dimensionality could prompt an issue of over fitting of the classifiers to the test information, known as the "curse of dimensionality". Anothercritical perspective to consider is the computational proficiency of the procedure, since the count of all the 263 highlights could be a container neck to accomplish continuous preparing. Consequently, a few creatorshaveproposeddistinctiveways to deal with selecting the best highlights from a lot of accessible factors. Thefundamental ruleofthesestrategiesis a blend of artificial neural networks (ANN) and a nature- inspired metaheuristic algorithm.The motivationbehindthe ANN is to give a classifier to the information tuples, which comprises in an established multilayer perception (MLP). The job of the second calculation is to control the progressive executions of the ANN, in an iterative way, until the ideal parameters of the system are chosen. The metaheuristic calculations were utilized in the investigations, offering ascend to half breed highlight determination approaches. Here we use Particle Swarm Optimization to train the neural network.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 824 4.4 ARTIFICIAL NEURAL NETWORK – PARTICLE SWARM OPTIMISATION Classification is the last stage in typical computer vision systems, such as those intended for fruit varietyrecognition. A set of tuple of features is given as input, the classifier is to predict the most likely class to which the given input tuple belongs to. In order to perform the task, the available data are divided and grouped into training and testing groups. The first part is used in training of the classifier, while the later is used to evaluate it's precision. In these cases, 60% of the samples of the three classes are randomly selected for the process of training and validation, and the remaining 40% of the samples are included in the testing group. The basic classifier in the proposed framework is a MLP neural network. MLPs are characterized by a progression of parameters which decide the type of the system: number of layers; number of neurons in each layer; transfer function in each layer; back-proliferation training sequence; and back propogation weight/inclination learning function. It is notable that proper tuning of these parameters can fundamentally influence the precision of the technique. To the component selection procedure, a hybrid approach is proposed to locate the ideal parameters of the MLP. The thought comprises in utilizing a metaheuristic calculation, which executes the MLP with various setups of the parameters. PSO is a promising strategy to prepare ANN. It isquickerand shows signs of improvement results as a rule. It helps in computing the system loads to locate the best reasonable answer for the given issue. PSO indicates quicker assembly. Particle swarm optimisation's outcomes present systems with great speculation on the informational indexes. Contrasted with evolutionary algorithms, PSO is quicker at drawing near to optima, however it can't adjust its speed step sizes for fine tuning. 5. IMPLEMENTATION Figure 2 - Block diagram of the proposed method for orange identification. Figure 2 shows the block diagram of the proposed method for orange identification. A total of 12 input images 4 for each type of oranges are taken. All of the images were captured with high-resolution digital camera with a resolution of 1280 * 960 pixels. The image is then resized into 256*256 pixels and the resized image is displayed in Figure 3. Figure 3 - Input images for three types of oranges The image of the fruit is then segmented from it’s background. Thresholding is the leastcomplextechniquefor segmenting an image and can be used to produce binary image from the gray scale image.Theorangesaresegregated and have a black background and therefore the segmentation issue is direct. The shape of object is characterized by their boundaries which are nothing but linked edges. Boundaries are used in the computation of the geometrical features such as size or orientation of the image. Here, sobel operator is used to perform the edge detection operation. After edge detection, binary representation is used to differentiate the image and it’s background. Morphological operations were applied to clean small noise areas and refine the shape of the objects. For this open and close operators were used and the image thus obtained is displayed in Figure 4. Figure 4 - Binary output of the oranges after edge detection. Feature extraction assumes a major role in computer vision algorithms, in light of the fact that a bigger quantities of Input image Pre- processing Feature extraction ANN-PSO classificationOutput
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 825 them builds the likelihood of discovering highlights that differences the diverse existing classes. For this reason, 263 features of three types have been found and taken into account. Some of the features are contrast, correlation, energy, entropy, homogeneity, Standard deviation, normalized value, mean and variance. And then, these are fed to the ANN-PSO network in order to identify the given image and the results are shown in Figure 5 Figure 5 - The results of the identified orange type. 6. CONCLUSIONS Exact and productive identification of natural products is a fundamental issue in post-gathering processes completed in business manufacturing plants. Programmed acknowledgment of organic productassortmentsrepresents an open test, because of the extraordinary comparability's that may exist between them. In this paper, another methodology for perceiving three regular orange assortments has beendisplayed,exhibitingit’sdownto earth practicality through a broad arrangement of trials. The proposed framework can be effectively inserted into the handling chain of a nourishment plant. To start with,natural products are fragmentedutilizinga satisfactorythresholding in the RGB space and mathematical morphology operators, accomplishing an exceptionally exact discovery of the oranges. The incredible preferred standpoint of the proposed methodology is that it tends to be specifically applied to different organic products, varieties and properties, since the choice of highlights and the setup of the system are naturally performed by metaheuristic calculations. 7. FUTURE WORK Different AI and soft computing strategies are to be utilized for natural product arrangement and deformitydiscoveryto give better quality item at the customer end for future work centers around the advances in programmed organic product characterization utilizing soft computing. The present system cannot classifyoverlappedimagesofobjects. Hence, in future, the system should be upgraded to identify overlapped objects. REFERENCES [1] "Artificial Neural Networks as Models of Neural Information Processing | Frontiers Research Topic". Retrieved (2018). [2] "Build with AI | DeepAI". DeepAI. (2018). [3] Kennedy, J., Eberhart, R. "Particle Swarm Optimization". (1995) [4] Particle Swarm Optimization - http://www.swarmintelligence.org/tutorials.php [5] Bianchi, Leonora; Marco Dorigo; Luca Maria Gambardella; Walter J. Gutjahr. "A survey on metaheuristics for stochastic combinatorial optimization". Natural Computing (2009). [6] Blum, C. Roli, A. "Metaheuristics in combinatorial optimization (2003). [7] Zhang Y, Wang S, Ji G, Phillips P. Fruit classification using computer vision and feed-forward neural network. J Food Eng (2014). [8] Zhang Y, Phillips P, Wang S, Ji G, Yang J, Wu J. “Fruit classification by biogeography-based optimization and feed-forward neural network.” Exp Syst (2016). [9] Wang S, Zhang Y, Ji G, Yang J, Wu J, Wei L. “Fruit classification by wavelet-entropy and feed-forward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization”. Entropy (2015). [10] Mohammadi V, Kheiralipour K, Ghasemi- Varnamkhasti M. “Detecting maturity of persimmon fruit based on image processing technique.” Sci Hortic-Amsterdam (2015). [11] Nasirahmadi A, Ashtiani S-HM. “Bag-of-featuremodel for sweet and bitter almond classification”. Biosyst Eng (2017). [12] Sofu MM, Erb O, Kayacan MC, Cetissli B. “Design of an automatic apple sorting system using machine vision.” Comp Electron Agric (2016). [13] Bonabeau E, Dorigo M, Theraulaz G. “Swarm intelligence: from natural to artificial systems” (1999). [14] Ciptohadijoyo S, Litananda WS,RivaiM,PurnomoMH. “Electronic nosebasedonpartitioncolumnintegrated with gas sensor for fruit identification and classification.” Computer Electron Agric (2016). [15] Garcı´a-Mateos G, Hernandez JL, Escarabajal- Henarejos D, Jae´n-Terrones S, Molina-Martı´nez JM. “Study and comparison of color models for automatic image analysis in irrigation management applications.” Agric Water Manage (2015). [16] Golzarian MR, Frick RA. “Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis.” Plant Meth (2011).
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