Plant Diseases Recognition Based on Image Processing Technology

In order to achieve intelligent, fast and accurate grading of apples, Apple's image acquisition and recognition system has been built, which is based on machine vision technology. Through color-to-gray, image noise reduction, image enhancement, and image segmentation processing of the collected image, three characteristics of the shape, size and color of the apple were recognized. Taking the 3 features of the apple identified from the image recognized as input, a multi-feature fusion classification method based on image processing and SVM was proposed in this paper. Through the constructed SVM vector machine, the apple's shape, size and color are synthesized to judge the apple's grade. The experimental results show that the classification method has the characteristics of high recognition rate and good real-time performance. This method lays the foundation for the application of machine vision technology in apple grading.

With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.

Early detection of gastric cancer dramatically improves patient outcomes, yet visual diagnosis during endoscopy remains operator-dependent and time-consuming. This paper presents an end-to-end Image Recognition and Diagnosis System for Early Gastric Cancer (IRDS-EGC) that combines deep convolutional neural networks for lesion detection, segmentation and classification on endoscopic images. We propose a hybrid architecture that ensembles a classification backbone (ResNet50 and EfficientNet-B3) with a U-Net-based segmentation head to localize suspicious regions and then aggregate features for final diagnosis. The system was trained and validated on a curated dataset of 10,000 annotated endoscopic images (3,500 early-gastric-cancer images, 6,500 benign) with clinically-informed augmentation and pre-processing. On a held-out test set (n = 1,500), the proposed system achieved a sensitivity of 94.3%, specificity of 90.3%, accuracy of 91.7%, and area under the ROC curve (AUC) of 0.955 for the binary task of early cancer vs non-cancer. Qualitative analysis shows the segmentation output closely aligns with clinician annotations. We discuss clinical integration pathways, limitations, and directions for future work including prospective validation and real-time endoscopic deployment.

The global anime market continues to grow, character recognition is particularly important in the derivatives market. And the demand for anime content on various platforms continues to increase. However, relatively few studies have been conducted on anime character image recognition, and the current research focuses on image recognition using a single ResNet50 model, with few studies attempting to incorporate other models to improve recognition accuracy. This project aims to construct a Pokémon image recognition system using deep learning techniques to improve classification efficiency and accuracy. The project uses the Pokémon image dataset on kaggle and performs data preprocessing, including data balancing, de-noising, normalization and data enhancement. The project constructed a classification prediction model combining ResNet50 and SVM respectively, which achieved 97.25% accuracy, 97.11% recall and 97.15% F1 score on the test set, indicating that the model can effectively recognize different kinds of Pokémon images. This project verifies the effectiveness of the ResNet50 model in image recognition tasks and provides a useful reference for subsequent research on image recognition.

This paper presents a color recognition and segmentation of color image through a combination of a multi-scale fuzzy system, an image edge based on Prewitt operator and morphological operators. In the proposed algorithm, after converting image from RGB color space to hue-saturation-value (HSV) color space, different scales of fuzzy sets in hue channel is constructed according to the people's psychological cognition to color. The image color would be quickly recognized fuzzily as less than ten kinds on scale one. To improve the performance of the color recognition rate, recognized image need to be identified on scale two to distinguish different colors observed by human eyes with the same color scheme. Then, adopting Prewitt operator to extract image edge information and applying a sequence of some morphological operators to the edge image, completing the multi-scale segmentation of image with the integration of color and edge information. Finally, indicating its area and species of specific color. The proposed algorithm can automatically adjusted in the process of color recognition and segmentation of image, it achieves recognition and segmentation without supervision. The obtained result has a good consistency with man's subjective visual perception.

This paper discusses effect of grouping on vector classifiers that are based on self-organising map (SOM). The SOM is an unsupervised learning neural network, and is used to form clusters using its topology preserving nature. Thus it is used for various pattern recognition applications. In image recognition, recognition accuracy is degraded under difficult lighting conditions. This paper proposes a new image recognition system that employs a grouping method. The proposed system does the grouping of vectors according to their brightness, and multiple vector classifiers are assigned to every groups. Recognition parameters of each classifier are tuned for the vectors belonging to its group. The proposed method is applied to position identification from images obtained from an on-board camera on a mobile robot. Comparison between the recognition systems with and without the grouping shows that the grouping can improve recognition accuracy.

At present, in medical diagnosis, facial acne vulgaris is a common disease, which mainly occurs in adolescents and has a large population base. For different degrees of facial acne, it needs to be classified and treated to relieve the pressure on medical resources. Normally, dermatologists use models that artificially determine the severity of acne vulgaris on the face. However, the outstanding problem is that it is a waste of manpower, and it may not be possible to quickly and reasonably grade acne treatment of different degrees. In this article, in view of the above problems, image recognition is used to judge the severity of facial acne vulgaris at different levels, so that perform hierarchical diagnosis and treatment. Therefore, this paper designs a graded diagnosis and treatment system for facial acne vulgaris based on image recognition. The system includes a camera acquisition system, an image processing system, an image recognition system, and related detection algorithms. In the processing process, the collected facial acne vulgaris images are mainly subjected to color processing, image segmentation processing and contour detection, and combined with Harris corner detection algorithm to detect acne. In order to determine the severity of acne and the site of the disease to a certain extent, and provide a reference for graded diagnosis and treatment.

Image feature extraction is an essential step in image recognition. In this paper, taking the benefits of the effectiveness of Gaussian-Bernoulli Restricted Boltzmann Machine GRBM for learning discriminative image features and the capability of Convolutional Neural Network CNN for learning spatial features, we propose a hybrid model called Convolutional Gaussian-Bernoulli Restricted Boltzmann Machine CGRBM for image feature extraction by combining GRBM with CNN. Experimental results implemented on some benchmark datasets showed that our model is more effective for natural images recognition tasks than some popular methods, which is suggested that our proposed method is a potential applicable method for real-valued image feature extraction and recognition.

Image processing and recognition methods are useful in many fields. According to situation, different techniques are used. For many years, methods based on optical Fourier transformation were very popular. Image recognition was performed generally by using optical correlators. Correlation techniques were strongly developed especially for military applications, but in many cases (industrial, biological and biomedical applications) these techniques suffer from a number of limitations. For these reasons, methods based on extraction and statistical processing of image features are more useful. Set of features can be extracted directly from an image (features based on image morphology, image moments etc.) or from image transforms (Fourier, Radon, Hough, Sine, Cosine etc.). The Fourier transformation is one of the most important in image processing. It can be simply performed by using an optical diffractometer. It allows to build image descriptors independent on image translation and after processing independent on image rotation. Diffractometers are very convenient in industrial and medical applications. Digital image processing and recognition were strongly developed on powerful workstations, however these procedures can also be implemented in PCs with DSP microprocessor cards or in situations where digital transforms used for image processing can be simply implemented and do not consume a lot of time. The example of biomedical image recognition performed in an optical way, by using a diffractometer, and in a digital system with a CCD camera will be described here.

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

To prevent needlestick injury and leftover instruments, and to perform efficient dental treatment, it is important to know the instruments required during dental treatment. Therefore, we will obtain a dataset for image recognition of dental treatment instruments, develop a system for detecting dental treatment instruments during treatment by image recognition, and evaluate the performance of the system to establish a method for detecting instruments during treatment. We created an image recognition dataset using 23 types of instruments commonly used in the Department of Restorative Dentistry and Endodontology at Osaka University Dental Hospital and a surgeon’s hands as detection targets. Two types of datasets were created: one annotated with only the characteristic parts of the instruments, and the other annotated with the entire parts of instruments. YOLOv4 and YOLOv7 were used as the image recognition system. The performance of the system was evaluated in terms of two metrics: detection accuracy (DA), which indicates the probability of correctly detecting the number of target instruments in an image, and the average precision (AP). When using YOLOv4, the mean DA and AP were 89.3% and 70.9%, respectively, when the characteristic parts of the instruments were annotated and 85.3% and 59.9%, respectively, when the entire parts of the instruments were annotated. When using YOLOv7, the mean DA and AP were 89.7% and 80.8%, respectively, when the characteristic parts of the instruments were annotated and 84.4% and 63.5%, respectively, when the entire parts of the instruments were annotated. The detection of dental instruments can be performed efficiently by targeting the parts characterizing them.

The goal of the paper is to present a novel approach for designing massively parallel architectures for image recognition systems. Massive parallel hardware produces high speed image recognition. Hardware architectures work much faster and are more effective for application specific processing than a software-based image recognition system. The concept of designing optimal image recognition systems with high speed, low complexity, good portability, and low cost has been present for a long time. The authors have considered the basic design factors, simple and regular design, concurrency and communication, and balancing computation with I/O. The technique presented provides a new method of designing an image recognition system by concentrating on modularity and massive parallelism. This is accomplished through the design of a processing element layer, local communicator layer, and network of new image layer. The processing element (PE) layer performs the basic recognition functions on individual pixels through logical operations. Both the local communicator and network of new image layers institute a mesh network topology, which has proven to be the fastest and most efficient. The local communicator layer has the most important function, it communicates with the other two layers, combines results from the PE layer, and houses the main control unit. All layers work simultaneously, or in other words, in a parallel fashion. The flow diagram of a CAD designer algorithm, based on this new design technique, is presented.

با برآورد ظرفیت تبادل کاتیونی با استفاده از ویژگی‏های پایه ای و زودیافت خاک می‏توان در وقت و هزینه صرفه‏جویی کرد. هدف این بررسی، مقایسه دو روش رگرسیون درختی و رگرسیون خطی چندگانه در برآورد ظرفیت تبادل کاتیونی (CEC) با استفاده از ویژگی‏هایخاک است. برای این منظور از داده های 106 نمونه خاک UNSODA استفاده شد. جهت برآورد CEC با استفاده از روش‏های رگرسیون درختی و رگرسیون خطی از ویژگی های اجزای بافت خاک،pH، ماده آلی و چگالی ظاهری استفاده شد. کارآیی روش رگرسیون درختی در برابر روش رگرسیون خطی چندگانه در برآورد CEC مقایسه شدند. نتایج نشان داد که در برآورد CEC با استفاده از روش های رگرسیونی تنها ضرایب ماده آلی (183/3) و درصد رس (274/0) که بیشترین همبستگی را با CEC دارند معنی دار شده و مدل رگرسیونی بر اساس این دو پارامتر توسعه یافت. همچنین از میان پارمترهای ورودی در روش رگرسیون درختی تنها پارامترهای ماده آلی و درصد رس در درخت رگرسیون ظاهر شد. روش رگرسیون درختی در دو مجموعه داده صحت سنجی و اعتبار سنجی بر اساس آماره های ارزیابی R2، RMSE، ME و GMER کارآیی بالاتری نسبت به روش های رگرسیونی خطی در برآورد CEC داشت. از میان روش های رگرسیونی خطی، مدل پیشنهادی کارآیی بالاتری نسبت به مدل های بل و ونکولن و بروسما و همکاران داشت.

In order to better extract image feature and recognition, a novel feature extraction algorithm of the binary image processing using Otsu combined with normalized moment of inertia (NMI) is put forward. Firstly, the image is processed into binary image and the target area is effectively segmented utilizing Otsu algorithm based on the criteria of maximal variance between-class , secondly, the NMI feature of the binary image is extracted, and finally, the extraction NMI feature is used in image recognition. Experimental results show that NMI feature of the binary image have the ability of anti-geometric distortions (translation, rotation and scaling, TRS), and anti-brightness distortions, the novel method have characteristics of simple extraction approach, little extraction parameter, easy implementation, and strong robustness.

This paper firstly analyzes and establishes the motion model of the quadrotor UAV, and then designs the image recognition and control system scheme for the path tracking task. In the image recognition part, the path information is obtained and the desired position signal is obtained by the steps of image optimization, Hough transformation, determining flight direction, identifying the drop-point area, and the PID control is used in the control system part. From the simulation results and 3D visualization module, it can be seen that the UAV can complete the straight-line flight, curve flight, and landing action well, and the image recognition and flight control scheme designed in this paper has good performance.