scholarly journals VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation

2018 ◽  
Vol 7 (4.11) ◽  
pp. 90 ◽  
Author(s):  
Mohamad Aqib Haqmi Abas ◽  
Nurlaila Ismail ◽  
Ahmad Ihsan Mohd Yassin ◽  
Mohd Nasir Taib
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Accuracy Score ◽  
Training Set ◽  
Plant Recognition ◽  
Plant Image ◽  
Validation Set ◽  
Overfitting Problem

This paper discusses the potential of applying VGG16 model architecture for plant classification. Flower images are used instead of leaves as in other plant recognition model because the structure of shape and color of leaves are similar in nature. This might be disadvantageous when we want to use only leaves images as a sole feature of plants to classify the species. Previous work has demonstrated the effectiveness of using transfer learning, dropout and data augmentation as a method to reduce overfitting problem of convolutional neural network model when applied in limited amount of images data. We have successfully build and train the VGG16 model with 2800 flower images. The model able to achieve a classification accuracy score of 96.25% for training set, 93.93% for validation set and 89.96% for testing set.  

scholarly journals The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries

Insects ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1134
Author(s):  
Mark T. Fowler ◽  
Rosemary S. Lees ◽  
Josias Fagbohoun ◽  
Nancy S. Matowo ◽  
Corine Ngufor ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Accuracy Score ◽  
Training Set ◽  
Egg Development ◽  
Test Set ◽  
Fertility Status ◽  
Mean Time

Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide.

scholarly journals Deep learning framework for material design space exploration using active transfer learning and data augmentation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yongtae Kim ◽  
Youngsoo Kim ◽  
Charles Yang ◽  
Kundo Park ◽  
Grace X. Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Design Space Exploration ◽  
Predictive Power ◽  
Design Space ◽  
Data Augmentation ◽  
Generative Models ◽  
Training Dataset ◽  
Initial Training ◽  
Active Transfer

AbstractNeural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

scholarly journals Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

2020 ◽  
Vol 11 ◽  
Author(s):  
Luning Bi ◽  
Guiping Hu
Keyword(s):  
Classification Accuracy ◽  
Plant Disease ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Overfitting Problem

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

scholarly journals Chili Plant Classification using Transfer Learning models through Object Detection

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 23-27
Author(s):  
Amirul Asyraf Abdul Manan ◽  
Mohd Azraai Mohd Razman ◽  
Ismail Mohd Khairuddin ◽  
Muhammad Nur Aiman Shapiee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Transfer Learning ◽  
Classification Performance ◽  
Experimental Result ◽  
Learning Models ◽  
Robotic Vision ◽  
Efficient Detection ◽  
Plant Image ◽  
Series Of Experiments

This study presents an application of using a Convolutional Neural Network (CNN) based detector to detect chili and its leaves in the chili plant image. Detecting chili on its plant is essential for the development of robotic vision and monitoring. Thus, helps us supervise the plant growth, furthermore, analyses their productivity and quality. This paper aims to develop a system that can monitor and identify bird’s eye chili plants by implementing machine learning. First, the development of methodology for efficient detection of bird’s eye chili and its leaf was made. A dataset of a total of 1866 images after augmentation of bird’s eye chili and its leaf was used in this experiment. YOLO Darknet was implemented to train the dataset. After a series of experiments were conducted, the model is compared with other transfer learning models like YOLO Tiny, Faster R-CNN, and EfficientDet. The classification performance of these transfer learning models has been calculated and compared with each other. The experimental result shows that the Yolov4 Darknet model achieves mAP of 75.69%, followed by EfficientDet at 71.85% for augmented dataset.

scholarly journals Perbandingan Convolution Neural Network Untuk Klasifikasi Kesegaran Ikan Bandeng Pada Citra Mata

2021 ◽  
Vol 8 (3) ◽  
pp. 601
Author(s):  
Eko Prasetyo ◽  
Rani Purbaningtyas ◽  
Raden Dimas Adityo ◽  
Enrico Tegar Prabowo ◽  
Achmad Irfan Ferdiansyah
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Animal Protein ◽  
Experimental Results ◽  
Convolution Neural Network ◽  
Food Ingredients ◽  
The People ◽  
Experimental Implementation

<p class="Abstrak">Ikan merupakan salah satu sumber protein hewani dan sangat diminati masyarakat Indonesia, dari survey bahan makanan yang diminati, bandeng peringkat keempat dibanding bahan makanan yang lain. Khususnya ikan bandeng, ikan ini menjadi satu dari enam ikan yang banyak dikonsumsi masyarakat selain tongkol, kembung, teri, mujair dan lele, maka ketelitian masyarakat ketika membeli ikan bandeng menjadi perhatian serius dalam memilih ikan bandeng segar. Deteksi kesegaran dengan menyentuh tubuh ikan dapat mengakibatkan kerusakan tanpa disengaja, maka deteksi kesegaran ikan harus dilakukan tanpa menyentuh ikan bandeng dengan memanfaatkan citra kondisi mata. Dalam riset ini, kami melakukan eksperimen implementasi klasifikasi kesegaran ikan bandeng sangat segar dan tidak segar berdasarkan mata menggunakan transfer learning dari empat CNN, yaitu Xception, MobileNet V1, Resnet50, dan VGG16. Dari hasil eksperimen klasifikasi dua kelas kesegaran ikan bandeng menggunakan 154 citra menunjukkan bahwa VGG16 mencapai kinerja terbaik dibanding arsitektur lainnya dimana akurasi klasifikasi mencapai 0.97. Dengan akurasi lebih tinggi dibanding arsitektur lainnya maka VGG16 relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan bandeng.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Fish, one source of animal protein, is an exciting food for Indonesia's people. From a survey of food-ingredients demanded, milkfish are ranked fourth compared to other food-ingredients. Especially for milkfish, this fish is one of the six fish consumed by Indonesia's people besides tuna, bloating, anchovies, tilapia, and catfish, so the exactitude of the people when buying is a severe concern in choosing fresh milkfish. Detection of freshness by touching the fish's body may cause unexpected destruction, so detecting the fish's freshness should be conducted without touching using the eye image. In this research, we conducted an experimental implementation of freshness milkfish classification (vastly fresh and not fresh) based on the eyes using transfer learning from several CNNs, such as Xception, MobileNet V1, Resnet50, and VGG16. The experimental results of the classification of two milkfish freshness classes using 154 images show that VGG16 achieves the best performance compared to other architectures, where the classification accuracy achieves 0.97. With higher accuracy than other architectures, VGG16 is relatively more appropriate for classifying two classes of milkfish freshness.</em></p>

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

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