LSGNet: A lightweight convolutional neural network model for tomato disease identification

Lightweight convolutional neural network model for field wheat ear disease identification

This work aims to construct and optimize the public emotion network communication model under the artificial intelligence (AI) environment and provide a new research direction for exploring public emotion network communication with intelligent human–computer interaction (HCI). The main challenge of this research is the lack of interactivity of intelligent human–computer expression design, and the accuracy of emotion recognition is not high. First, the theoretical analysis of network public opinion is made, and the network public opinion calculation method and emotion recognition method based on deep learning are proposed. On this basis, the public emotion network communication model is established, and the model’s communication mechanism and rules are described in detail. Next, the emotion calculation of human–computer expression interaction and the emotion expression mode of the humanoid robot are proposed, which provides an experimental basis for HCI and public emotion network communication analysis. According to the data of email network and microblog network in the real network, the network degree distribution map is established. The statistical results show that email and microblog networks belong to power law distribution, and the aggregation coefficient is relatively high. Microblog network degree changes slowly, and more points are scattered in the middle area. In a practical sense, when the positive and negative emotions in the public emotion network communication model are input into the bionic robot, the robot can show happy and lost expressions. The results show that compared with the traditional convolutional neural network (CNN) model or recurrent neural network (RNN) model, the RNN–CNN structure model of RNN combined with CNN reduces the waiting time by about 20% and improves the algorithm accuracy by at least 3.1%. The public emotion network communication model based on deep learning and intelligent HCI can accurately reflect the public emotion state in the network, which provides a practical basis for the application of AI technology in network public opinion judgment.

The main goal of this research was to accurately diagnose diseases in pepper plants using convolutional neural network (CNN) models and visual parameters features. Thus, we aimed to build a computing system that can utilise visual cues to identify plant diseases with almost 100% reliability. A total of 2,200 images were trained and tested on a dataset, which was divided into healthy leaves and those with bacterial ailments. The images were preprocessed, resized to 300x300 pixels, and then segmented to extract the relevant traits from them. This CNN model, in a word, is accurate because it detects diseases with 99.65% accuracy. If we want to speak of a high accuracy rate, that's the CNN model. It learns to identify the intricate patterns and features in pepper plants that are associated with diseases. CNN can make revolutionary changes to the identification of plant diseases and help prevent crop damage through timely treatments. Automatic identification of the disease makes life easier for both agriculturists and scientists. The results underscore the importance of image preprocessing, segmentation, and feature extraction in improving the ability of CNN models to locate diseases. Additionally, the performance of the CNN model was compared with other machine learning models such as KNN, Decision Tree, ANN, and Random Forest. It was found that the developed CNN model can predict the response with greater accuracy than the others. Modern high-tech precision farming systems might utilise proposed CNN models for plant disease diagnosis.

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Rice is the staple food in Bangladesh. Every year many crops are lost because of the disease of the rice plant. Our farmers are facing a great loss every year as well as rice productivity is declining. Some diseased and healthy leaf images of rice plants have been collected. The images have been classified into four classes. The classes are Brown Spot, Hispa, Leaf Blast diseased leaves, and Healthy leaves, usually known as Oryza Saiva diseases of rice. Deep learning is very common and popular to analyze and make predictions from these kind of image data. In this research, the CNN (Convolutional Neural Network) models have been used to predict the classes. Among various CNN models, VGG16, MobileNet, ResNet50, and DenseNet121 pre-trained models have been applied. Among those pre-trained models, the highest accuracy 95.3% has been found from the VGG16 model. In addition, an Android mobile app has been developed using the highest performant VGG16 trained model. Using this mobile app, farmers will be able to upload a photo and predict the disease of that rice plant or not in a convenient way.

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

Deep learning theory demonstrates its great power in the complicated data processing field. As a prevalent deep learning technology, convolutional neural network (CNN) has achieved some successful applications in the field of fault detection and classification in the complicated industrial systems because of its strong nonlinear feature extraction capability. However, the basic CNN views all the features equally and can not highlight these features which play a more important role in the fault classification procedure. To deal with this problem, this paper proposes an improved CNN model, called multi-head attention CNN (MACNN), to distinguish the importance of different features for better fault classification performance. In the MACNN framework, the raw training data are firstly analyzed by the multiple convolutional layers for feature extraction. Then, the features obtained by the convolution layers are used as the input of the multi-head attention (MA) layers. The introduction of the MA mechanism can help exploit more meaningful information by emphasizing the important features. Lastly, the softmax classifier is utilized to classify the fault patterns. To test the method performance, one case study on the benchmark Tennessee Eastman industrial system is performed and the results show that MACNN gives the better fault classification results than the traditional feedforward neural network (FNN) and CNN models.

The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Apple leaf diseases can cause significant crop failure and impact the economy of farmers and the agricultural industry. With the increasing demand for quality apples, it is important to develop effective and efficient solutions to detect apple plant diseases early. This research aims to develop an automated system that can identify diseases in apple plants based on leaf images using the Convolutional Neural Network (CNN) model. This model was developed with the ResNet50V2 architecture to classify four leaf conditions: three types of common diseases and one healthy condition. This research applies the CNN model for leaf image processing and the Waterfall system development method. The stages start from needs analysis by collecting data to be processed by the cnn model, interface design of the classification system, program code implementation, and functionality testing using black-box testing. CNN model development includes the stages of collecting datasets sourced from Malang apple plantations as many as 150 images and Kaggle public datasets totalling 3,071 images, then image preprocessing, model development and training. Our research results produced an apple plant disease classification system by implementing the CNN model. Based on the results of testing the system and the model used, it shows that the CNN model applied in the system achieves a classification accuracy of 99.01%, and the functionality of the system built runs well.

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

Topology-aware mamba for crack segmentation in structures

Hyperspectral image classification based on spatial and spectral kernels generation network

- This paper presents a comparative analysis between a traditional convolutional neural network (CNN) model and a hybrid ResNet50 + AdaBoost architecture for non-invasive cardiovascular risk prediction using retinal fundus images. The baseline model, trained on a small dataset for binary classification, achieves reasonable accuracy but lacks scalability and granularity. In contrast, the proposed model leverages deep residual learning for feature extraction and ensemble-based classification to predict five distinct cardiovascular risk levels. Evaluated on a class-balanced dataset of over 11,500 images, the hybrid model achieved a testing accuracy of 91.48% and a macro F1-score of approximately 0.93. The system also integrates data visualization and database logging to support clinical explainability. The findings demonstrate the proposed method’s superiority in risk stratification, robustness, and deployment readiness for real-world applications. Key Words: Retinal Imaging, Cardiovascular Risk Prediction, ResNet50, AdaBoost, Deep Learning, Ensemble Learning, Fundus Images, Heart Attack Classification

In our life, emotions often have a profound impact on human behavior, especially for drivers, as negative emotions can increase the risk of traffic accidents. As such, it is imperative to accurately discern the emotional states of drivers in order to preemptively address and mitigate any negative emotions that may otherwise manifest and compromise driving behavior. In contrast to many current studies that rely on complex and deep neural network models to achieve high accuracy, this research aims to explore the potential of achieving high recognition accuracy using shallow neural networks through restructuring the structure and dimensions of the data. In this study, we propose an end-to-end convolutional neural network (CNN) model called simply ameliorated CNN (SACNN) to address the issue of low accuracy in cross-subject emotion recognition. We extracted features and converted dimensions of EEG signals from the SEED dataset from the BCMI Laboratory to construct 62-dimensional data, and obtained the optimal model configuration through ablation experiments. To further improve recognition accuracy, we selected the top 10 channels with the highest accuracy by separately training the EEG data of each of the 62 channels. The results showed that the SACNN model achieved an accuracy of 88.16% based on raw cross-subject data, and an accuracy of 91.85% based on EEG channel data from the top 10 channels. In addition, we explored the impact of the position of the BN and dropout layers on the model through experiments, and found that a targeted shallow CNN model performed better than deeper and larger perceptual field CNN models. Furthermore, we discuss herein the future issues and challenges of driver emotion recognition in promising smart city applications.