Fruit Recognition by Hierarchical Temporal Memory

Intelligent Automation & Soft Computing, 2022

Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multifeature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) deep learning applications, a multi-model fruit image identification system was created. For performance assessment in terms of accuracy analysis, the proposed framework is compared to ANFIS, RNN, CNN, and RNN-CNN. The motivation for adopting deep learning is that these models categorize pictures without the need for any intervention or process. The suggested fruit recognition method offers efficient and promising results, according to the findings of the experiments in terms of accuracy and F-measure performance analysis.

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.

International Journal of Engineering Research and, 2020

The technology popularly used in this innovative time is Computer vision for the process of fruit recognition. In comparison to other machine learning (ML) algorithms, such networks like deep neural networks (DNN), provide results to identify fruits in images. And there results are promising. As for now, in order to identify fruits, different DNN-based classifications of algorithms are used. However, there issue in ordering to recognizing fruits has yet to be addressed. And it is due to similarities in size, shape and other features. This section discusses this in brief. The deep learning is used for recognizing fruits and its other applications. This section will also provide a concise and precise explanation of convolution neural networks (CNNs). Moreover, it also throws light on the EfficientNet architecture to recognize fruit by using the Fruit 360 dataset. The results show that the proposed model is 95% more accurate.

2019

Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of the complexity and similarity. In this paper, a fruit recognition system using CNN is proposed. The proposed method uses deep learning techniques for the classification. We have used Fruits-360 dataset for the evaluation purpose. From the dataset, we have established a dataset which contains 17,823 images from 25 different categories. The images are divided into training and test dataset. Moreover, for the classification accuracies, we have used various combinations of hidden layer and epochs for different cases and made a comparison between them. The overall performance losses of the network for different cases also observed. Finally, we have achieved the best test accuracy of 100% and a training accuracy of 99.79%.

Fruits classification is demanded in some fields, such as industrial agriculture. Automatic fruit classification from their digital image plays a vital role in those fields. The classification encounters several challenges due to capturing fruits' images from different viewing angle, rotation, and illumination pose. In this paper a framework for recognition and classification of fruits from their images have been proposed depending on texture features, the proposed system rely on three phases; firstly, pre-processing, as images need to be resized, filtered, color convert, and threshold in order to create a fruit mask which is used for fruit's region of interest segmentation; followed by two methods for texture features extraction, first method utilize Local Binary Pattern (LBP), while the second method uses Principal Component Analysis (PCA) to generate features vector for each fruit image. Classification is the last phase; two supervised machine learning algorithms; K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are utilized to identity and recognize the fruits images classes. Both methods are tested using 1200 fruits images, from 12 classes acquired from Fruits-360 database. The results show that combining LBP with K-NN, and SVM yields the best accuracy up to 100% and 89.44% respectively, while the accuracy of applying PCA with K-NN and SVM reached to 86.38 % and 85.83% respectively.

Journal of Telecommunication, Electronic and Computer Engineering, 2017

Fruit classification process is gaining importance in image processing applications specifically in agricultural area. However, classification process is challenging for images captured in natural environment due to the existence of nonuniform illumination. Different illuminations produce different intensity on the object surface and thus lead to inaccurate classification. Therefore, this study focuses on the improvement of development of classification model for images captured in natural environment. This study has developed a neural network (NN) model that is able to classify objects based on their surface colour. The result of the NN model shows that, with the network configuration of 6-7-4, the NN model works very well for objects exposed to the natural illumination. To justify the proof-of-concept, the proposed classification model is tested on jatropha fruit images and the results show that the developed model is able to classify the fruit accurately.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.

Intelligent Computing Systems, 2018

At this time, great effort is being directed toward developing problem-solving technology that mimic human cognitive processes. Research has been done to develop object recognition using Computer Vision for daily tasks such as secure access, traffic management, and robotic behavior. For this research, four different machine learning algorithms have been developed to overcome the computer vision problem of object recognition. Hierarchical temporal memory (HTM) is an emerging technology based on biological methods of the human cortex to learn patterns. This research applied an HTM algorithm to images (video sequences) in order to compare this technique against two others: support vector machines (SVM) and artificial neural networks (ANN). It was concluded that HTM was the most effective.

Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing - ICMSSP 2019, 2019

Intra-class recognition of fruit using image processing and computer vision techniques, is considered challenging task due to similarities between various type of fruits and external environmental changes such as lighting. Mainly sub-type of the same fruit shows a much similarities between each other so, it's more difficult task to distinguish than when different types with same color of fruits are involved. It creates mismatches between training and test set. The problem become more difficult when lighting changes which tend to change the actual characteristic of the fruits like contour shape. To solve the problem of intra-class recognition, we proposed a deep neural network model with only few layers which learn optimal features from an input image adaptively. Our proposed method has been tested on 2 different fruit types and 2 sub classes of each fruit. To show the robustness of the classifier on sub classes of fruits. Extensive experiments have been done on dataset of 5602 apple fruit images & 4292 kiwi fruit images. The proposed model shows satisfactory performance across different fruit types and sub-types.