Automatic Fruits Identification System Using Hybrid Technique

An automatic fruit recognition system and a review of previous fruit detection work are reported. The methodology presented is able to recognize spherical fruits in natural conditions facing di$cult situations: shadows, bright areas, occlusions and overlapping fruits. The sensor used is a laser range-"nder giving range/attenuation data of the sensed surface. The recognition system uses a laser range-"nder model and a dual color/shape analysis algorithm to locate the fruit. The three-dimensional position of the fruit, radius and the re#ectance are obtained after the recognition stages. Results for a set of arti"cial orange tree images and real-time considerations are presented.

IOP Conference Series: Earth and Environmental Science

Food processing Technologies play important roles in supporting Making Indonesia (MI) 4.0. The effectiveness in food processing becomes emerging in the food industry. The automatic fruit classification system is becoming important for use in many food processing industries. A novel approach for classifying, using support vector machines (SVM) is presented in this paper. Fruit classification based on their shape is proposed in this work. The system differentiates the fruit based on their shape. Fast Fourier Transform (FFT) is extracted and later used as input to the SVM-based identifier. The fruit parameters are compared and classified, the results of computer simulation show that this technique produces better accuracy than that of the existing technique that is based on artificial neural network (Ann). The SVM also requires less training time than artificial neural network (Ann).

—The paper presents an automated system for classification of fruits. A dataset containing five different fruits was constructed using an ordinary camera. All the fruits were analyzed on the basis of their color (RGB space), shape and texture and then classified using different classifiers to find the classifier that gives the best accuracy. Gray Level Co-occurance Matrix(GLCM) is used to calculate texture features .Best accuracy was achieved by support vector machine (svm). All the processing was carried out in Matlab.

International Journal of Computer Applications, 2016

Intra-class recognition of fruits using image processing and pattern recognition techniques, is a challenging task mainly because sub-types of the same fruit show a large amount of similarities between each other and hence more difficult to distinguish than when different types of fruits are involved (inter-class). The problem becomes more acute when the camera viewpoint also changes which tend to change the known characteristics of the fruits like contour shape. To solve this problem, this paper proposes a view point invariant solution for intra-class recognition of fruits by combining color and texture features and using a Neural Network (NN) classifier. Experimentations done on a dataset of 270 fruit images show satisfactory performance across different fruit types and sub-types.

Current Journal of Applied Science and Technology, 2021

Automated defect detection of fruits using computer vision and machine learning concepts has ‎become a significant area of research. In ‎this work, working prototype hardware model of conveyor with PC is designed, constructed and implemented to analyze the fruit quality. The prototype consists of low-cost microcontrollers, USB camera and MATLAB user interface. The automated classification model rejects or accepts the fruit based on the quality i.e., good (ripe, unripe) and bad. For the classification of fruit quality, machine learning algorithms such as Support Vector Machine, KNN, Random Forest classifier, Decision Tree classifier and ANN are used. The dataset used in this work consists of the following fruit varieties i.e., apple, orange, tomato, guava, lemon, and pomegranate. We trained, tested and ‎compared the performance of these five machine learning approaches and found out that the ANN based fruit detection performs better. The overall accuracy obtained by the ANN model for...

Recent developments in machine vision have opened a wide range of applications, and farming is no exception. Deep learning (DL) has a wide range of applications because of its capacity to extract robust features from photos. The shape, color, and feel of many fruit species make it difficult to discover and classify fruits. When examining the effects of artificial intelligence on fruit identification and classification, the Author noted that, up until 2018, the majority of approaches relied on traditional machine learning (ML) techniques, while just a few ways took use of DL techniques for recognizing fruits and categorization. In this paper, the Author thoroughly covered the datasets that many academics utilized, the useful descriptors, the application of the model, and the difficulties of utilizing DL to identify and classify fruits. Finally, the Author compiled the outcomes of various DL techniques used in earlier research to identify and categorize fruits. This work examines the use of models based on DL for fruit categorization and recognition in recent studies. In order to make it simpler for beginning agricultural researchers to comprehend the importance of ML in the agricultural domain, the Author have developed a DL model for apple categorization using the well-known dataset "Fruit 360" starting from scratch. The recently proposed model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%).

2017

1,2,3,4 Student, Dept. of Electronics and Telecommunication Engineering, K. J. Somaiya Institute of Engineering and Information Technology, Maharashtra, Mumbai. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Fruits should be quickly and correctly differentiated from their surroundings for the fruit harvesting robot. Edge based and color based detection methods are generally used to segment images of fruits obtained under natural lighting conditions. In this work, Digitized images of mango fruits along with its background were selected from the Internet in order to find a mango in each image and to locate its exact position. We compared the results of Edge based and colored based segmentation results and found that color based segmentation outperforms the edge based segmentation in all aspects. The comparison results are shown in the segmented image results. Accordingly, a new mango ...

Turkish Journal of Agriculture - Food Science and Technology, 2021

In this study, an image processing algorithm and classification unit were developed to classify the fruits according to their size and color characteristics. For this purpose, a total of 300 fruits (50 fruit samples from each of the Starkrimson Delicious and Golden Delicious apple varieties, Washington Navel and Valencia Midknight orange varieties, Ekmek and Eşme quince varieties) were used in the experiments. The size and color values measured with a caliper and a spectrophotometer were entered in the developed image processing algorithm to determine the success rates of classifying the fruits. The integration of image processing algorithm with the classification unit classified 88%, 100%, 96%, 82%, 86%, respectively. On the other hand, the size and color values read in fruits with the image processing algorithm were evaluated using predictive techniques used in data mining. For this purpose, K Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes classification and Multilayer Pe...

2014

Nowadays computer science is getting more and more involved in agricultural and food science. Various AI and soft computing techniques are used for fruit classification and defect detection to provide better quality product at the consumer end. This article focuses on the advances in automatic fruit classification using soft computing techniques for ten types of fruit viz. apple, dates, blueberries, grapes, peach, pomegranate, watermelon, banana, orange, and mango. Keywords— Automatic Fruit Classification, Soft Computing, Image Processing, Neural Networks, Fuzzy Logic.