scholarly journals GRADING WARNA DAUN TEMBAKAU BAWAH NAUNGAN MENGGUNAKAN JARINGAN SARAF TIRUAN. (Color Grading of Shaded Tobacco Leaves Using Artificial Neural Network)

2017 ◽  
Vol 12 (1) ◽  
pp. 43-57
Author(s):  
Aneke Rintiasti ◽  
Ikhwan Krisnadi
Keyword(s):  
Neural Network ◽  
Raw Material ◽  
Classification Model ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Leaf Analysis ◽  
Color Grading ◽  
Leaf Image ◽  
Rgb Model

Various cigars, which are present in the community among the elite and prestigious venues, the raw material is a Java Tabak cigars, tobacco from Java, especially Klaten and Jember. Recent years, the availability of labor more difficult with increasing costs skyrocketing, so it must start leading to mechanization. The purpose of this research was to Generate Design of Tobacco Leaf Analysis Applications, Getting Segmentation Model for pixel readout from tobacco leaves, Generate classification models that can be used for the separation of tobacco leaves which is expected to ease the process of evaluation and classification of color in the first sorting Tobacco leaves. Tobacco Leaf used is The Under Shade Tobacco leaf (TBN) consisted of five classes, namely the color Blue / Green (B), Yellow (K), Yellow Sprayed (KV), Red (M), Red Sprayed (MV). Before analyzed the leaves image photographed using a cabinet that unaffected the outside light. TBN leaf image is then analyzed using the RGB model and models HSV, RGB image of the model  is  analyzed using the characteristic leaf color values, The image of leaf TBN that meets the characteristics become an input of Bakcpropagation Neural Networks with the target are 5 color grade which converted into a binary form. The research resulted Segmentation Model for pixel readout TBN tobacco leaves using RGB models, classification model that can be used for the classification of TBN leaves use Neural Network Back Training RGB with an error value = 8.7%.”keywords : besuki tobacco, shaded tobacco, image processingABSTRAK Aneka cerutu, yang hadir di kalangan komunitas elit dan tempat-tempat yang prestisius, bahan bakunya adalah Java Tabak Cerutu, tembakau asal Jawa, khususnya Klaten dan Jember. Beberapa tahun belakangan ini, ketersediaan tenaga kerja semakin sulit den gan biaya yang semakin meroket, sehingga harus mulai mengarah ke mekanisasi. Tujuan Penelitian ini adalah menghasilkan Rancang Bangun Aplikasi Analisa Daun Tembakau, mendapatkan Model Segmentasi untuk pembacaan piksel daun tembakau, menghasilkan Model Klasifikasi yang dapat digunakan untuk Pemisahan daun tembakau,sehingga diharapkan dapat mempermudah proses evaluasi dan klasifikasi warna pada Sortasi I daun Tembakau. Daun Tembakau yang digunakan adalah Daun Tembakau Bawah Naungan (TBN) jenis besuki terdiri dari 5 kelas warna yaitu Biru / Hijau (B), Kuning (K), Kuning Tidak Merata (KV), Merah (M), Merah Tidak Merata (MV). Sebelum dianalisa citra daun difoto menggunakan cabinet yang tidak terpengaruh cahaya luar. Citra daun TBN tersebut kemudian dianalisa menggunakan model RGB, dari model RGB citra daun dianalisa menggunakan karakteristik nilai warna, citra daun TBN yang memenuhi karakteristik menjadi masukan Jaringan Saraf Tiruan Bakcpropagation dengan target 5 kelas warna yang sudah diubah menjadi bentuk biner. Penelitian menghasilkan Model Segmentasi untuk pembacaan piksel daun tembakau TBN menggunakan model RGB, Model Klasifikasi yang dapat digunakan untuk klasifikasi daun TBN menggunakan Neural Network Back PropagationTraining RGB dengan nilai error = 8.7%.Kata Kunci : tembakau besuki, tembakau bawah naungan, pengolahan citra 

An improved lightweight network architecture for identifying tobacco leaf maturity based on Deep learning

2021 ◽  
pp. 1-10
Author(s):  
JX Li ◽  
H Zhao ◽  
S.P Zhu ◽  
H. Huang ◽  
YJ Miao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Superior Performance ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Network Information ◽  
Leaf Maturity ◽  
Model Size

The classification of fresh tobacco leaves during the picking process plays an important role in the subsequent roasting. In this paper, a lightweight convolutional neural network is used to detect the maturity of tobacco leaves quickly. Fresh tobacco leaves in the datasets are divided into 3 categories by the picking position, and each category is divided into 4 maturity levels and finally gets 12 types of tobacco leaves with different maturity. To ensure the lightweight of the model, the new network is based on the MobileNetV2 to establish. By utilizing shortcut operation, the shallow network information is preserved, and network degradation is suppressed. In the tobacco leaf datasets we obtained, the improved network has superior performance and compared with other classic networks, the model size and the number of operations have been reduced.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision

2021 ◽  
Vol 65 (1) ◽  
pp. 11-22
Author(s):  
Mengyao Lu ◽  
Shuwen Jiang ◽  
Cong Wang ◽  
Dong Chen ◽  
Tian’en Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Model ◽  
List Type ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Grading Model

HighlightsA classification model for the front and back sides of tobacco leaves was developed for application in industry.A tobacco leaf grading method that combines a CNN with double-branch integration was proposed.The A-ResNet network was proposed and compared with other classic CNN networks.The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency.Abstract. Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

An Artificial Neural Network Classification of Prescription Nonadherence

2020 ◽  
pp. 487-501
Author(s):  
Steven Walczak ◽  
Senanu R. Okuboyejo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Healthcare Providers ◽  
Classification Model ◽  
Survey Method ◽  
Neural Network Classification ◽  
Treatment Plans ◽  
Ann Models ◽  
Artificial Neural

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals Automated Optic Disc Segmentation and Classification Model using Optimal Convolutional Neural Network for Glaucoma Diagnosis System

2019 ◽  
Vol 9 (1) ◽  
pp. 7555-7561
Keyword(s):  
Neural Network ◽  
Optic Disc ◽  
Optimal Parameter ◽  
Median Filter ◽  
Pso Algorithm ◽  
Classification Model ◽  
Automated Identification ◽  
Glaucoma Diagnosis ◽  
Color Fundus Image

In present days, Glaucoma is an important disease which affects the retinal portion of the eye. The identification of Glaucoma in a color fundus image is a difficult process and it needs high experience and knowledge. The earlier identification glaucoma could save the patient from blindness. An important way to diagnose the glaucoma is to detect and segment the optic disc (OD) area. The region of OD area finds useful to help the automated identification of abnormal functions occurs in the case of any injury or damage. This paper presented an automated OD segmentation and classification model for the detection of glaucoma. The presented model involves feature extraction using median filter, segmentation using morphological operation and classification using convolution neural network (CNN). Here, optimal parameter settings of the CNN are automatically tuned by the use of particle swarm optimization (PSO) algorithm. The presented model is validated using DRISHTI-GS dataset and a detailed quantitative analysis is made to ensure the goodness of the presented model. In addition, the extensive simulation outcome pointed out that the presented model showed outperforming results with the maximum accuracy of 97.02% in the classification of OD.

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