Wheat Disease Recognition through One-shot Learning using Fields Images

One-Shot Learning for Custom Identification Tasks; A Review

Siamese neural network (SINN) is an image processing model that compares the scores of two patterns. The SINN algorithm is a combination of the use of the double convolutional neural network (CNN) algorithm. By combined SINN with a one-shot learning algorithm, we can build an image model without requiring thousands of images for training. The test results from the SINN algorithm and one-shot learning show that this process was successful in matching the two data but was unable to produce labels from the data being tested. Because of this, the researcher decided to continue the implementation process using the CNN algorithm combined with single shot detection (SSD). By using a dataset of 5000, the recognition and translation of the Toba Batak script was successful. The percentage of average accuracy results from CNN and SSD in recognizing Toba Batak characters is 84.08% for single characters and 74.13% for mixed characters. While the percentage of average accuracy results for testing the breadth first search algorithm is 75.725%.

Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.

Offline signature verification is one of the most challenging tasks in biometric authentication. Despite recent advances in this field using image recognition and deep learning, there are many remaining things to be explored. The most recent technique, which is Siamese convolutional neural network, has been used a lot in this field and has achieved great results. This paper presents an architecture that combines the power of Siamese Triplet CNN and a fully-connected neural network for binary classification to automatically verify genuine and forgery signatures even if the forged signature is highly skilled. On the challenging public dataset for signature verification BHSig260, the proposed model can achieve a low False Acceptance Rate = 13.66, which is slightly better than the reference model. Based on this approach, the one-shot learning should make it possible to determine if the input image is genuine or fraudulent just from one base image. Therefore, our model is expected to be extremely suitable for practical problems, such as banking systems or mobile authentication applications, in which the amount of data for each identity is limited in quantity and variety.KeywordsOne-shot learningOffline signature verificationSiamese convolutional neural networkTriplet loss

Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction. Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses. For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates. Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis. This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction. This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.

One-Shot and Few-shot Learning algorithms have emerged as techniques that can imitate a humans ability to learn from very few examples. This is an advantage over traditional deep networks which require a lot of training samples and lack of robustness due to their excessive domain specific discriminators. In this paper, we explore a one-shot learning approach to recognizing handwritten words using Siamese networks to classify the handwritten images at the word level. The Siamese network’s ability to compute similarities between two images is learned using a supervised metric but the fully trained Siamese network can be used to classify new data that has previously not been used to train the network. The model learns to discriminate inputs from a small labelled support set. By using a convolutional architecture we were able to achieve robust results. We also expect that training the system over a larger distributions of data will result in improved general handwritten word classification. Accuracy as high as 92.4% was obtained while performing 5-way one-shot word recognition on a publicly available dataset which is quite high in comparison to the state-of-the-art methods.

Though deep neural networks have played a very important role in the field of vision-based hand gesture recognition, however, it is challenging to acquire large numbers of annotated samples to support its deep learning or training. Furthermore, in practical applications it often encounters some case with only one single sample for a new gesture class so that conventional recognition method cannot be qualified with a satisfactory classification performance. In this paper, the methodology of transfer learning is employed to build an effective network architecture of one-shot learning so as to deal with such intractable problem. Then some useful knowledge from deep training with big dataset of relative objects can be transferred and utilized to strengthen one-shot learning hand gesture recognition (OSLHGR) rather than to train a network from scratch. According to this idea a well-designed convolutional network architecture with deeper layers, C3D (Tran et al. in: ICCV, pp 4489–4497, 2015), is modified as an effective tool to extract spatiotemporal feature by deep learning. Then continuous fine-tune training is performed on a sample of new classes to complete one-shot learning. Moreover, the test of classification is carried out by Softmax classifier and geometrical classification based on Euclidean distance. Finally, a series of experiments and tests on two benchmark datasets, VIVA (Vision for Intelligent Vehicles and Applications) and SKIG (Sheffield Kinect Gesture) are conducted to demonstrate its state-of-the-art recognition accuracy of our proposed method. Meanwhile, a special dataset of gestures, BSG, is built using SoftKinetic DS325 for the test of OSLHGR, and a series of test results verify and validate its well classification performance and real-time response speed.

Deep Learning (DL) and Artificial Intelligence (AI) tools have shown great success in different areas of medical diagnostics. In this paper, we show another success in orthodontics. In orthodontics, the right treatment timing of many actions and operations is crucial because many environmental and genetic conditions may modify jaw growth. The stage of growth is related to the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM to determine the suitable timing of the treatment is important. In orthodontics, lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features. Nowadays, ML and AI tools are used for many medical and biological diagnostic imaging. This paper reports on the development of a Deep Learning (DL) Convolutional Neural Network (CNN) method to determine (directly from images) the degree of maturation of CVM classified in six degrees. The results show the performances of the proposed method in different contexts with different number of images for training, evaluation and testing and different pre-processing of these images. The proposed model and method are validated by cross validation. The implemented software is almost ready for use by orthodontists.

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

Prediction and Recognition of plant disease in the early stage is one of the most essential needs to increase agriculture, which plays an important role in our country's economy and helps to feed a large population. And with the help of earlier detection, we can save the plants and avoid losses. Deep learning techniques are used widely to classify or predict diseases by using images. This paper proposed a hybrid model of deep learning to classify the sunflower diseases, i.e. Alternaria leaf blight, Downy mildew, Phoma blight, and Verticillium wilt. To make a hybrid model I used the stacking ensemble learning technique and combine two models i.e. VGG-16 and MobileNet, We also make our own dataset with Google images, and our proposed model gave 89.2% accuracy on our dataset, which is better than the other models.

In the last few years, mechanical fault diagnosis (FD) based on deep learning has been systematically applied to various industrial fields. However, most methods rely on enough labeled data, but collecting enough sample of failure is timeconsuming and labor-intensive. It remains challenging to train a model for FD with numbered training data and to function correctly under intricate operating scenario. In response to this problem, the FD method based on Siamese neural network (SNN) has shown promising results in recent years, especially in the case of insufficient training samples; the fault diagnosis work has achieved good results and has been widely concerned by researchers at domestic and foreign. To better combine the SNN for fault diagnosis and give full play to the advantages of the SNN in fault diagnosis, this paper summarizes and analyzes the fault diagnosis methods based on SNN. Firstly, the basic framework of the SNN method is introduced, and the advantages and application scenarios of the framework are pointed out. Secondly, the existing fault diagnosis methods based on SNN are classified, including the principle of the technique, improved methods, and shortcomings. Finally, the FD methods based on SNN are summarized, and the development of the SNN in FD is in prospect.

“Food is the most important thing for the people”, Food is intricately linked to both the national economy and the livelihood of the people, serving as a vital material for our daily existence. Wheat, standing as one of the three core grain crops, holds paramount importance in safeguarding national food security. However, the wheat planting process remains constantly exposed to a diverse array of environmental factors, ranging from the intensity of light to fluctuations in temperature, soil fertility, fertilizer application methods, and water availability. Occasionally, these variables trigger diseases and insect infestations that can seriously affect wheat yield and quality if not promptly and effectively addressed. Therefore, it is imperative to manage these challenges in a timely and effective manner, ensuring the safety and integrity of wheat production, which in turn guarantees the stability of our national food supply. Traditional methods of manual detection of pests and diseases mainly rely on naked eye observation and manual statistics. Such solutions are highly subjective, have low timeliness, and difficult to unify precision. With the development of computer technology and deep learning, more and more research and applications have been carried out to address the shortcomings of traditional manual detection methods. In this study, deep learning is combined with the application of disease and insect pest recognition. Studying wheat powdery mildew, scab, leaf rust, and midge, convolutional and capsule networks are investigated for pest recognition, establishing an image recognition system for wheat diseases and pests.

This paper presents an approach for matching various sizes of excavated wooden fragments based on siamese neural networks. We propose a siamese network composed of global average pooling and a fine-tuned Resnet encoder (GA-S-net). We also propose an elaborated siamese network by replacing the global average pooling with a spatial pyramid pooling layer and add a new dense absolute difference layer (SP-S-net). Samples of 37,760 fragments were prepared from 268 complete wooden tablets excavated from the Heijo-Kyo Palace ruins used during the Nara period in Japan. Both of the networks answer whether two fragments are from the same tablet or not. The result of both networks for the testing set is similar to AUC (Area under the curve) of ROC (Receiver Operating Characteristic) curve being around 90%. In AUC of large fragments, however, SP-S-net is better than GA-S-net with 97.1% versus 93.8%. These networks are rather new for dealing with various sizes of inputs for the matching problem.

A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks

Face recognition has recently garnered the interest of scientists due to its wide-ranging applications (such as law enforcement, medicine, business, consumer goods, government, and public safety). This biometric approach is both the quickest and least intrusive compared to others. In present face recognition systems, Convolutional Neural Networks (CNNs) are utilized to retrieve rich attributes for the classification network. Using convolution models, implicit and locally-focused interactions can be channeled (except the ones at top-most layers). In this study, a Squeeze-and-Excitement block is added after each convolution layer to clearly express channel interdependencies and increase the network's sensitivity to significant input. We resort to One-shot Learning using a Siamese Network since there is frequently insufficient data available in real-world situations. Contrastive Loss is utilized for the Siamese network, whereas Triplet Loss is used for the CNN network. The outcomes of our experiment demonstrate cutting-edge efficacy. Our ablation research demonstrates that incorporating a Squeeze-and-Excitation building block enhances network functionality.