Shape-based Fruit Recognition and Classification

Jana, Susovan; Parekh, Ranjan

doi:10.1007/978-981-10-6430-2_15

Cited by 23 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, there are a number of works which focus on a single feature of fruit and not consider more than one feature. For example, some feature base fruit recognition models, like [20] focuses on color chromaticity, model [21] focuses on wavelet, model [22] focuses on shape feature and only consider recognizing fruit without detecting or locating it from the global image. In this paper, by global image we refer an image with multiple objects and rich background where a local image is an image with single object and simple background.…”

Section: A Fruit Detection and Classification Using Selected Featurementioning

confidence: 99%

“…3(a), are taken with a very noisy background which can totally distract the machine learning model and can point out a completely wrong target object and the entire algorithm can be failed. But, models like [20,21] and [22] do not consider fruit images with reach background. Most of the existing models work better on an image with single fruit in white background.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

Khan¹,

Debnath²

2019

IJIGSP

View full text Add to dashboard Cite

In this paper, an efficient approach has been proposed to localize every clearly visible object or region of object from an image, using less memory and computing power. For object detection we have processed every input image to overcome several complexities, which are the main limitations to achieve better result, such as overlap between multiple objects, noise in the image background, poor resolution etc. We have also implemented an improved Convolutional Neural Network based classification or recognition algorithm which has proved to provide better performance than baseline works. Combining these two detection and recognition approaches, we have developed a competent multi-class Fruit Detection and Recognition (FDR) model that is very proficient regardless of different limitations such as high and poor image quality, complex background or lightening condition, different fruits of same shape and color, multiple overlapped fruits, existence of non-fruit object in the image and the variety in size, shape, angel and feature of fruit. This proposed FDR model is also capable of detecting every single fruit separately from a set of overlapping fruits. Another major contribution of our FDR model is that it is not a dataset oriented model which works better on only a particular dataset as it has been proved to provide better performance while applying on both real world images (e.g., our own dataset) and several states of art datasets. Nevertheless, taking a number of challenges into consideration, our proposed model is capable of detecting and recognizing fruits from image with a better accuracy and average precision rate of about 0.9875.

show abstract

Section: A Fruit Detection and Classification Using Selected Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

Khan¹,

Debnath²

2019

IJIGSP

View full text Add to dashboard Cite

show abstract

“…Fruit recognition uses fruit image's content for analysis, identification, and classification in the agriculture domain. Some existing works such as recognition of various fruits [16][17][18][19][20][21] or even focusing on specific fruits such as olive [22], persimmon [23], almond [24], apple [25], papaya [26], and durian [27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Authors in [17] represent fruit images based on few shape features such as area perimeter of the fruit region, major axis length, minor axis length and distance between the foci of an equivalent ellipse, width, height and area of minimum bounding box, and area and perimeter of smallest convex polygon. These shape features allow for the image representation to be invariant to translation, rotation, uniform scaling, surface features, and growth stage of fruit.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For feature extraction, the most suitable feature descriptor has to be constructed based on the targeted domain since feature extraction methods are domain specific. The works done by [16][17][18] [22][23][24][25][26] show that the feature descriptors used are different and unique based on the respective fruit. The feature descriptor will also need to be invariant to rotation and translation or illumination to increase the robustness of the proposed work.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

Mustaffa

Abdullah

et al. 2018

MJCS

View full text Add to dashboard Cite

Many fruit recognition approaches today are designed to classify different type of fruits but there is little effort being done for content-based fruit recognition specifically focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same colour from green to yellowish brown with just slightly different shape and pattern of thorns. Therefore, it is hard to differentiate them with the current methods. It would be valuable to have an automated content-based recognition framework that can automatically represent and recognise a durian species given a durian image as the input. Therefore, this work aims to contribute to a new representation method based on multiple features for effective durian recognition. Two features based on shape and texture is considered in this work. Simple shape signatures which include area, perimeter, and circularity are used to determine the shape of the fruit durian and its base while the texture of the fruit is constructed based on Local Binary Pattern. We extracted these features from 240 durian images and trained this proposed method using few classifiers. Based on 10-fold cross validation, it is found that Logistic Regression, Gaussian Naïve Bayesian, and Linear Discriminant Analysis classifiers performed equally well with 100% achievement of accuracy, precision, recall, and F1-score. We further tested the proposed algorithm on larger dataset which consisted of 42337 fruit images (64 various categories). Experimental results based on larger and more general dataset have shown that the proposed multiple features trained on Linear Discriminant Analysis classifier able to achieve 72.38% accuracy, 73% precision, 72% recall, and 72% F1-score.

show abstract