Automatic fruit recognition from natural images using color and texture features

Jana, Susovan; Basak, Saikat; Parekh, Ranjan

doi:10.1109/devic.2017.8074025

Cited by 74 publications

(33 citation statements)

References 10 publications

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“…The purpose of [8] is to recognize the fruits but classifier and accuracy are not mentioned in the work though they have used large data size. In the paper [9] is develop a system to recognize the fruit with the help of SVM but accuracy and segmentation technique is not mentioned. In the paper [10] developed an expert system for fruit and plant recognition using SVM.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…In this fruit recognition process, they have classified the fruits using minimum distance criterion and not mentioned the accuracy of the proposed methodology. Jana et al [9] developed a methodology to classify different types of fruits using texture and color features based on statistical color features and gray-level co-occurrence matrix (GLCM). To classify the fruits images, SVMs are used in the methodology.…”

Section: Literature Reviewmentioning

confidence: 99%

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Computer Vision Based Local Fruit Recognition

Mia¹,

Mia²,

Majumder³

et al. 2019

IJEAT

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Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research.

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Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Local Fruit Recognition

Mia¹,

Mia²,

Majumder³

et al. 2019

IJEAT

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show abstract

“…Candra sekhar [8] juga merancang sebuah sistem yang mendeteksi tingkat kematangan pada buah mangga. Prediksi tingkat kematangan buah dilakukan dengan memanfaatkan video yang dikumpulkan oleh kamera Charge Coupled Device (CCD) pada conveyor yang membawa mangga.…”

Section: Pendahuluanunclassified

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Subhan¹,

Basri²

2019

Intek

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Fakfak is the number one nutmeg producing area in Indonesia with a land area of around 16,011 Ha. Nutmeg production is projected to continue to increase until 2020, recorded in 2011 the production of nutmeg in particular reached 12,884 tons or 25 percent of the total Indonesian nutmeg production. This increase was not followed by an increase in market share. One solution to get a wider nutmeg market is exports. But currently not all requests for nutmeg can be fulfilled, because the quality of the nutmeg does not meet the requirements requested. One factor is the surface defects in the nutmeg skin that affects the quality of the fruit, especially the appearance of the fruit. The sorting of nutmegs has so far been using conservative methods, namely by observation based on experience (self-taught). This manual method is felt to be less effective because it depends on the conditions and conditions of the sorting staff, different perceptions between each sorter, takes a long time, requires large costs and involves many workers. To deal with these problems, our previous research developed a method for classifying nutmeg seeds, using image processing methods with color and shape parameters combined with a neural network and sigmoid convolution classification algorithm as a validation method, an accuracy of 87%, but this has not been said to be optimal so we try to use the same approach with the latest method improvements using the R-CNN Faster obtained the best accuracy of 95% with a learning rate of 4000 with a processing time of 0.04 minutes per second.

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“…The authors suggest working the fruits counting process by extending this process for the future scope. [5] Ilyas et al (2018) proposed the robust, accurate and flexible method to identify the varieties of fruits by using color, shape, size and texture features. This works with three main processes: acquisition and cropping of images, extraction of features, and classification.…”

Section: Introductionmentioning

confidence: 99%

Fruit Recognition Using Color and Morphological Features Fusion

San¹,

Aung²,

Khaing³

2019

IJIGSP

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It is still difficult to recognize the kind of fruit which are of different colors, shapes, and textures. This paper proposes a features fusion method to recognize five different classes of fruits that are the images from the fruit360 dataset. We are processed with four stages: preprocessing, boundary extraction, feature extractions, and classification. Pre-processing is performed to remove the noise by using the median filter, and boundary extraction are operated with the morphological operation. In feature extraction, we have extracted two types of features: color, and morphological features of the image. Color features are extracted from the RGB color channel, and morphological features are extracted from the image that detected the boundary of fruit by using morphological operations. These two types of features are combined in a single feature descriptor.

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Automatic fruit recognition from natural images using color and texture features

Cited by 74 publications

References 10 publications

Computer Vision Based Local Fruit Recognition

Computer Vision Based Local Fruit Recognition

Klasifikasi Mutu Buah Pala (Myristica Fragrans Houtt) Berbasis Pengolahan Citra Menggunakan Metode Deep Learning Arsitektur Faster R-CNN

Fruit Recognition Using Color and Morphological Features Fusion

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