Apple disease classification using color, texture and shape features from images

Dubey, Shiv Ram; Jalal, Anand Singh

doi:10.1007/s11760-015-0821-1

Cited by 154 publications

(63 citation statements)

References 23 publications

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“…For example, SVM [7] represents as the classical method of machine learning algorithms in the literature. Dubey et al proposed a disease classification method for apple fruit which based on image processing technology [14]. To segment the images of grape from background and leaves, Behroozi-Khazaei and Maleki presented an method based on genetic algorithm (GA) and artificial neural network (ANN) [15].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Zhang¹,

Jia²,

Li³

et al. 2019

KSII TIIS

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Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

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Section: Related Workmentioning

confidence: 99%

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Zhang¹,

Jia²,

Li³

et al. 2019

KSII TIIS

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show abstract

“…Apples were classified into clean and severe categories with 5-8% false positive and negative ratios. Dubey et al (2016) investigated an image processing based approach for the apple disease classification using color, texture and shape based features. The approach is composed of four steps .i.e.…”

Section: Fruitsmentioning

confidence: 99%

Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review

Chopde¹,

Patil²,

Shaikh³

et al. 2017

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Quality inspection of food is a tedious and labor intensive process. Ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards has raised the need for accurate, fast and objective quality determination methods. Manual quality inspection is a slow, costly, unreliable process and suffers from poor repeatability. Computer vision provides one alternative for an automated, non-destructive and costeffective technique to accomplish these requirements. Computer vision is a rapid, economic, consistent, objective inspection and evaluation technique. Computer vision has been successfully adopted for the quality analysis of meat and fish, fruits, vegetables and bread with applications ranging from routine inspection to the complex vision guided robotic control. The paper presents the recent developments in computer vision technology along with important aspects of image processing techniques coupled with application of computer vision technology in quality inspection of fruits and vegetables.

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“…They tested these features on support vector machine (SVM), artificial neural network (ANN), k-nearest neighborhood (k-NN), linear discriminant classifier (LDC), and AdaBoost classifiers and distinguished the stem-calyx regions of apples from the bruised areas with 94% accuracy using the SVM classifier. Other authors [4] conducted a study on the detection of apple diseases using color, texture, and shape-based features. They used L*a*b* color space in the segmentation of the defects on the apple's surface and took 320 apple images with 4 class labels with different diseases as the dataset.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Hekim¹,

Cömert²,

Adem³

2020

Turk J Elec Eng & Comp Sci

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In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.

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Apple disease classification using color, texture and shape features from images

Cited by 154 publications

References 23 publications

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

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