Image-based deep learning automated sorting of date fruit

Nasiri, Alireza; Taheri‐Garavand, Amin; Zhang, Yudong

doi:10.1016/j.postharvbio.2019.04.003

Cited by 154 publications

(54 citation statements)

References 25 publications

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“…Our study and a reference study by Altaheri, H., M. Alsulaiman, et al [13] used the same datasets in a farm environment and the date fruit bunches in an orchard, whereas other studies used different datasets using single dates with uniform background. Table 7 illustrates a comparison of the evaluation parameters of the proposed system and the reference study of Nasiri, A., A. Taheri-Garavand, et al [12]. In the proposed system, VGG-19 outperformed the other models and showed outstanding results for all performance metrics for all maturity detection systems.…”

Section: Discussionmentioning

confidence: 95%

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IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

et al. 2020

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Section: Discussionmentioning

confidence: 95%

“…Several studies have been done to classify date fruits. Nasiri A., A. Taheri-Garavand, et al [12] used computer vision and machine ML techniques to classify three maturity stages (Khalal, Rutab, and Tamar) and one defective stage. The dataset was built using single dates with a uniform background.…”

Section: Literature Reviewmentioning

confidence: 99%

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

et al. 2020

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“…Histogram and texture features were extracted from monochrome images to classify dates into soft and hard fruit using linear discriminant analysis (LDA) and artificial neural network (ANN) to obtain 84% and 77% accuracy, respectively. A simple CNN structure was used to separate healthy and defective dates and predict the ripening stage of the healthy dates [27].…”

Section: Date Skin Quality Evaluationmentioning

confidence: 99%

Smart Camera for Quality Inspection and Grading of Food Products

et al. 2020

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Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.Electronics 2020, 9, 505 2 of 18 or sorting machines for food products on the market have been designed to automate these tasks and have proven to be very successful.Many recent developments were aimed at broad grading applications. For example, a machine learning-based system was built to use color and texture features for tomato grading and surface defect detection [1]. Color and texture features were collected for maturity evaluation, defect detection, size and shape analysis for mangoes [2]. Guava fruits were classified into four maturity levels using the K-nearest neighbor (KNN) algorithm to analyze color distribution in HSI (Hue, Saturation, and Intensity) color space [3]. Image processing techniques were used for corn seed grading [4], plum fruit maturity evaluation [5], quality assessment of pomegranate fruits [6], and the ripeness of palm fruit [7]. Two grading systems for date maturity [8] and skin quality [9] evaluation were designed specifically for Medjool dates. All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture fe...

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“…The results with more false negatives indicate the algorithm performance on the considered dataset. Deep CNN [11] developed to combine the stages of feature selection and classification. Its focus was to discriminate the date fruits as healthy or unhealthy.…”

Section: Literature Reviewmentioning

confidence: 99%

Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network

Ramya*,

Rani

2019

IJRTE

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Acute Myelogenous Leukemia (AML) is a type of disease associated with acute leukemia which is getting increased in both children’s and adults. AML falls under the category of cancer disease. The term acute in AML indicates rapid progression of disease in human body. The main challenge of medical field in vision of computer and multimedia is texture and color between various categories. The variation in texture and color attributes makes the classification task a tedious. Deep learning has shown its dazzling performance in various streams, which includes classification too. The objective of image classification is to differentiate the subcategories that belong to same basic-level category. The main objective of this paper is to propose bioinspired based on convolutional neural network to classify the microscopic blood images for AML. This paper has utilized bioinspired concept to extract the features more reliably. Bench mark performance metrics were chosen to evaluate the proposed classifier against the previous classifiers based on two parameters. The results indicate that the proposed classifiers has outperformed the previous works towards the classification of AML.

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Image-based deep learning automated sorting of date fruit

Cited by 154 publications

References 25 publications

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

Smart Camera for Quality Inspection and Grading of Food Products

Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network

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