Classification and Grading of Multiple Varieties of Apple Fruit

Bhargava, Anuja; Bansal, Atul

doi:10.1007/s12161-021-01970-0

Cited by 44 publications

(23 citation statements)

References 26 publications

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“…In recent years, image processing and recognition techniques have been developed and applied in many fields such as automatic apple picking ( Zhang et al, 2016 ; Tao and Zhou, 2017 ; Kang et al, 2020 ), non-destructive detection of apple fruit quality ( Zou et al, 2010 ; Zhang et al, 2014 ; Li Y. F. et al, 2021 ), automatic apples grading ( Huang and Fei, 2017 ; Bhargava and Bansal, 2021 ), and apple yield estimation ( Qian et al, 2013 ; Li Z. J. et al, 2021 ). These techniques can be used as references to carry out automatic identification and diagnosis of apple diseases.…”

Section: Discussionmentioning

confidence: 99%

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Qin

Wang

et al. 2022

Front. Plant Sci.

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Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.

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Section: Discussionmentioning

confidence: 99%

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Qin

Wang

et al. 2022

Front. Plant Sci.

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“…60% accuracy (Kumar et al, 2015). Bhargava and Bansal (2021) developed an automated system based on k nearest neighbour algorithm (KNN), LR, Sparse Representations Classification (SRC), and Support Vector Machine (SVM) classifiers to distinguished fresh and rotten apple fruits. First, they used grab-cut method and fuzzy c-means clustering to segment the images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Later on, they extracted multiple features statistical, textural, geometrical, discrete wavelet transform, a histogram of the oriented gradient, and Laws' texture energy and selected by principal component analysis from the feature space. Later on, proposed cross validation techniques achieved 92.90% (k = 5), 98.42% (k = 10), and 95.27% (k = 15) accuracy by SVM classifier (Bhargava and Bansal, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Shape and texture based classification of citrus using principal component analysis

et al. 2021

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Citrus family consists of a variety of eatable, consumable and usable items with varying nutritional contents. Naked eye citrus classification needs expert human effort, which provides poor decision reliability. The unreliable classification decision may be extremely hazardous when the citrus is being classified for exports or usage in pharmacy products and various food items. In this paper, citrus fruit has been classified on shape and texture features. Principal Component Analysis (PCA) was used as a methodology to explore statistical findings. The average accuracy of the system proposed is 84%. This system can be implemented on pharmacy stores, food production units, or industries, and citrus export centers for reliable citrus fruit classification.

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“…Bhargava et al, aiming at the problem of apple (fresh, rotten), six different varieties of apple (Fuji, York, Golden Crown, Red Crown, Granny Smith, and Jonagold) were selected. Firstly, the fruit region in the image was segmented by grab-cut method and Fuzzy C-Means Clustering, and then six features were extracted from feature space by principal component analysis to train an SVM classifier, and finally, 98.42% classification accuracy was obtained [18]. In [19], Ponce et al focused on the variety identification of olive fruits.…”

Section: Introductionmentioning

confidence: 99%

Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

2021

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Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN. Then, the deep features of the input images were extracted by a specific deep features layer of the CNN. Next, two kinds of deep features from different networks were fused by CCA to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused features. When applied to an open dataset, the model outcome shows that the fused deep features with any combination can obtain better identification performance than by using a single type of deep feature. The fusion of fc6 (in AlexNet network) and Fc1000 (in ResNet50 network) deep features obtained the best identification performance. The average F1 Score of 96.9% was 8.7% higher compared to the best performance of a single deep feature, i.e., Fc1000 of ResNet101, which was 88.2%. Furthermore, the F1 Score of the proposed method is 2.7% higher than the best performance obtained by using a CNN directly. The experimental results show that the method proposed in this paper can achieve fast and accurate identification of grape varieties. Based on the proposed algorithm, the smart machinery in agriculture can take more targeted measures based on the different characteristics of different grape varieties for further improvement of the yield and quality of grape production.

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Classification and Grading of Multiple Varieties of Apple Fruit

Cited by 44 publications

References 26 publications

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Shape and texture based classification of citrus using principal component analysis

Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

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