Automatic Detection and Grading of Multiple Fruits by Machine Learning

Bhargava, Anuja; Bansal, Atul

doi:10.1007/s12161-019-01690-6

Cited by 80 publications

(24 citation statements)

References 41 publications

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“…Algoritma yang digunakan adalah k Nearest Neighbor (kNN), Support Vector Machine (SVM), sparse representative classifier (SRC), dan artificial neural network (ANN). Hasil penelitian menunjukkan SVM mencapai akurasi tertinggi dengan akurasi 98,48% [3]. Penelitian serupa untuk mengklasifikasikan buah tomat dilakukan dengan menggunakan algoritma KNN, multi layer perceptron (MLP), dan k-Means clustering.…”

Section: Pendahuluanunclassified

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Rismiyati

Luthfiarta

2021

Telematika

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Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.

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Section: Pendahuluanunclassified

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Rismiyati

Luthfiarta

2021

Telematika

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“…This system was tested by two datasets with maximum accuracy of 96.81% and 93.00%, respectively. Bhargava and Bansal 24 presented a system to detect the quality of apple, avocado, banana, and orange by fuzzy C-means clustering with accuracy of 95.72% using SVM. But the above methods are only for defect detection, and do not carry out more detailed classification.…”

Section: Introductionmentioning

confidence: 99%

Apple quality identification and classification by image processing based on convolutional neural networks

Feng

Liu

et al. 2021

Sci Rep

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This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple’s quality detection and classification.

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“…As for the extracted crop features, the features input to the classifier can achieve the crops classification and detection. In the choice of the classifier, Supports Vector Machine (SVM) [5,21], Artificial Neural Network (ANN) [22,23], Random Forest (RF) [24,25], and deep learning methods [26][27][28] are widely used. The segmentation algorithm based on deep learning can get a better segmentation effect, but it needs many training samples.…”

Section: Introductionmentioning

confidence: 99%

Cotton stubble detection based on wavelet decomposition and texture features

et al. 2021

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Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

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Automatic Detection and Grading of Multiple Fruits by Machine Learning

Cited by 80 publications

References 41 publications

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Apple quality identification and classification by image processing based on convolutional neural networks

Cotton stubble detection based on wavelet decomposition and texture features

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