scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

scholarly journals Classification of large green chilli maturity using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012009
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Maturity Index ◽  
Maturity Stage ◽  
Non Destructive

Abstract Chili (Capsicum annuum L.) is the source of various nutraceutical small molecules, such as ascorbic acid (vitamin C), carotenoids, tocopherols, flavonoids, and capsinoids. The purpose of this study was to classify the maturity stage of large green chili into three maturity levels, i.e. maturity 1 (maturity index 1 / 34 days after anthesis (DAA)), maturity 2 (maturity index 3 / 47 DAA), and maturity 3 (maturity index 5 / 60 DAA) by using convolutional neural networks (CNN) based deep learning and computer vision. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. From the overall sensitivity analysis results, the highest maturity classification accuracy of large green chili was 93.89% which can be achieved when using GoogLeNet with SGDmoptimizer and learning rate of 0.00005. However, in further testing using testing-set data, the highest classification accuracy based on confusion matrix was reaching 91.27% when using the CNN SqueezeNet model with RMSProp optimizer and a learning rate of 0.0001. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the maturity of large green chili with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

scholarly journals Deep Learning to Detect and Classify the Purity Level of Luwak Coffee Green Beans

2021 ◽  
Vol 30 (1) ◽  
pp. 1-18
Author(s):  
Yusuf Hendrawan ◽  
Shinta Widyaningtyas ◽  
Muchammad Riza Fauzy ◽  
Sucipto Sucipto ◽  
Retno Damayanti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Low Cost ◽  
Confusion Matrix ◽  
Optimization Technique ◽  
Learning Rate ◽  
Production Costs ◽  
Green Beans ◽  
Initial Learning ◽  
Palm Civet

Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs, Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method’s development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, ResNet-50, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001, providing an accuracy average of up to 85.00%. Later, the CNN model can be used to establish a real-time, non-destructive, rapid, and precise purity detection system.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

scholarly journals Classification of Brain Tumor IDH Status using MRI and Deep Learning

2019 ◽  
Author(s):  
Sahil Nalawade ◽  
Gowtham Murugesan ◽  
Maryam Vejdani-Jahromi ◽  
Ryan A. Fisicaro ◽  
Chandan Ganesh Bangalore Yogananda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Validation Data ◽  
Mutation Status ◽  
Radiologic Imaging ◽  
Address Data

AbstractIsocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

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