A Leaf Type Recognition Algorithm Based on SVM Optimized by Improved Grid Search Method

At present, photovoltaic power generation is becoming more and more important in the field of new energy power generation. The traditional BP neural network algorithm is prone to fall into local extreme points in the training process of the short-term photovoltaic power prediction model, and the training model If the global optimum is not reached, the optimization is stopped, and the weights of the initial network are randomly selected, which leads to problems such as low model prediction accuracy and low iteration efficiency. This paper proposes a short-term photovoltaic power prediction algorithm based on improved BP neural network. The method uses the Levenberg-Marquardt algorithm to replace the traditional gradient descent method for training, which improves the iterative speed of the traditional BP neural network training, and uses the genetic algorithm to optimize the initial weight of the network model, which further improves the model prediction accuracy. The simulation results show that the prediction accuracy of the improved algorithm for short-term photovoltaic power is significantly better than that of the algorithm before the improvement, and the stability and iterative efficiency are greatly improved.

Manual plant species classification requires more time, effort, and professional knowledge and also, and they are very costly. At present days, researchers utilized deep learning techniques for the classification of plants over plant images. The deep learning models attain great success, and then, the lack of interpretability set a limit on their application. To overcome these limitations, they utilized measurable, interpretable, and computer-aided features from plant leaf images. Image processing became very complex and crucial at the time of feature extraction. There are nearly 391,000 vascular plant species present in the world widely. So, the classification and identification of plants became complex and impractical for professionals. Most of the plant species have huge similarities and it consumes a huge amount of time for classification. So, it is essential to develop a computerized system for the identification and classification of plants. A great advancement is widely developed in science and technology for the recognition and classification of plant species in biological fields. Thus, the automatic plant leaf detection models are utilized to help professionals and botanists to classify plant species rapidly. This work proposes a novel automatic plant leaf image classification method through a deep learning algorithm. At first, the input images are gathered from the standard dataset, where the deep features from the gathered images are extracted using Resnet and VGG16. The extracted features are fused and fed to the classification stage. The plant leaf image classifications are done through the Bidirectional Long Short-Term Memory (Bi-LSTM). The empirical outcomes of the developed model have achieved better performance regarding precision and accuracy.

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

In view of the rapid growth of municipal solid waste (MSW), a large number of MSW are transported to the outside of the city for landfill or incineration, only part of the MSW is treated innocuously, the speed of MSW treatment is slow, and the level of garbage classification intelligence is low, this paper proposes an algorithm of MSW classification and recycling based on deep learning technology, and uses convolution neural network to build garbage intelligence simultaneous interpreting and classification algorithm, which improves the accuracy and speed of garbage image recognition. The algorithm is compared with the traditional BP neural network algorithm. The simulation results show that the algorithm is 30% faster than the traditional algorithm. The classification algorithm has faster response speed, higher accuracy and stronger robustness.

With the development of deep learning, the use of neural network for text detection has been more in-depth research and more widely used. Based on this, this paper studies the Chinese text event detection technology based on improved neural network. In the research, this paper uses the flower pollination algorithm (FPA) to improve the traditional BP neural network algorithm. By optimizing the weights and thresholds of BP neural network, a Chinese text event detection method based on improved neural network is proposed. In order to verify the effect of the Chinese text event detection method based on improved neural network, this paper compares it with the natural scene text detection method, and compares the recall rate, accuracy rate and time-consuming. The results show that the accuracy rate of the natural scene text detection method is 88%, and the recall rate is 73%. The accuracy rate of the text detection method based on the improved neural network is 95% and the recall rate is 86%. The F value of the natural scene text detection method in the Chinese text event detection test is 0.79, which takes 4.56s The value of F in is 0.90, which takes 0.64s. Therefore, the Chinese text event detection method based on improved neural network has better performance.

In this paper, wireless sensors are used to design and build a location management system for smart aging, and the optimization of this system is analysed. The function and structure of the positioning system are designed and implemented. Next, the system commissioning process and field implementation results are summarized and analysed to guarantee the reliability and integrity of the system. Finally, the system requirements and implementation are integrated to realize the basic requirements of the IoT‐based elderly positioning management system and reserve the interface for later expansion. Finally, the system excellence and functional usability tested through unit testing as well as comprehensive testing. In this paper, a theoretical analysis of the prediction algorithm is carried out for the disease prediction module in the health monitoring software of the smart senior care system. To improve the accuracy of the prediction, the traditional BP neural network algorithm is optimized using DS evidence theory, thus fusing multiple sets of prediction results obtained from the BP neural network into a more accurate set of data, and the performance of the algorithm before and after the improvement is compared. The IoT‐based home health management for the elderly starts from the health service demand of the elderly, explains the basic concept of IoT technology and home health management for the elderly, and analyses the feasibility of home health management for the elderly and the advantages of IoT technology in‐home health management for the elderly; through the field research, the IoT‐based home health management platform for the elderly is carried out from three aspects of users, business, and technology. The design covers the platform architecture and functional modules of the IoT‐based senior home health management platform, which can solve the problem that the elderly can spend a comfortable life in their old age without going home.

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered.

The automatic classification of plant species using leaf’s images is a rather complex task because of the high variability of species and the high inter-species similarity of the descriptors to be extracted. In order to extract robust descriptors, convolutional neural networks combine several architectures adapted to the extraction of shape and texture descriptors. These increasingly complex architectures are generally transpositions to the problems of the classification of plant species of models previously trained on image bases. As a result, the performance of these models is linked to the relevance of the database used. In this paper, we propose a new simple architecture capable of simultaneously extracting shape and texture descriptors in a single model. This model is based on the use of several different resolution levels of the energy layer initially introduced by Andrearczyk et al. in the analysis of dynamic textures. This makes it possible to efficiently extract the shape and texture descriptors in a single architecture of convolutional neural networks, less complex in terms of the number of parameters to be determined. The tests carried out on Flavia give us good classification rates of 99%. They are 97.10% on MalayaKew D1, 99.68% on MalayaKew D2, and 97.00% on LeafSnap. These results show the excellent capabilities of our model on some of the most commonly used image databases.

An electrochemical method for plant species determination and classification based on fingerprinting petal tissue

Protection of biodiversity is quite essential and for this purpose we should know more about the species. Identification of plant species by using conventional hand-crafted features is complex. It is difficult for non-experts to remember the specific botanical terms. The idea of automatic identification of plant species is approaching reality. Machine learning and deep learning play an important role in this matter. The deep learning Convolutional Neural Networks (CNN) can apply to extract the features from leaf images. CNN or machine learning classifiers can be used for the classification of plant species. The deep learning methods outperform all handcrafted methods. This survey is about the identification of plant species using deep learning methods.

This paper proposes a data-driven classification model for traditional Chinese medicinal herbs based on mid-infrared spectral data. Addressing the limitations of traditional identification methods when the herbs’ appearance is damaged or incomplete, this study employs machine learning techniques to achieve accurate classification of medicinal herb types through spectral data preprocessing and feature extraction. Firstly, the Savitzky-Golay convolution smoothing method and Standard Normal Variate (SNV) transformation were used for denoising the spectral data. Then, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional spectral data and extract the key features. Finally, the Gaussian Mixture Model (GMM) was applied to cluster the reduced data, categorizing the medicinal herbs into six classes. The results show that this method produces the accurate and stable classification. The constructed model is not only applicable to the classification and origin identification of medicinal herbs but also provides an important reference value for the classification and origin identification of other plant species.

Pollen grains of plant species have unique morphological characteristics. The characteristics, such as variability in pollen shape, size, and microscopic surface features, can be effectively used in the identification of plant species. This approach can be helpful in the identification of medicinal plant species that have a rich biodiversity. This study presents a computer vision-driven framework designed for the segmentation and classification of pollen images acquired through scanning electron microscopy (SEM). This framework has broad applications in palynological fields, including taxonomy, paleoecology, and biodiversity monitoring. While the methodology is universally applicable, we illustrate its effectiveness using a representative dataset consisting of SEM images from 28 medicinal plant species, selected for their morphological diversity and availability. The dataset features 269 images dedicated to segmentation and 5842 images for classification across 28 distinct classes, which have been compiled into an open-access resource titled MPalyn (Medicinal Pollen and Palynology SEM Database). This study demonstrates a broad application in offering a scalable and adaptable tool for automated pollen analysis, which has the potential to advance research in different fields of palynology.

Plant recognition is a vital and challenging task. Leaf recognition plays a vital role in plant recognition and its key issue lies in whether selected features are solid and have a good potentiality to prejudice between different kinds of leaves. Leaf teeth are one of the most essential and intricate aspects of the leaf that are employed in automatic plant identification systems to classify and identify plant species. Automatic species identification has a huge amount of advantages over traditional species identification. Presently, most plant automatic identification strategies focus on shape, venation and texture, a feature unremarkably utilized in ancient species identification that is unnoticed. Different species of leaves have distinct characteristics that aid in the classification of certain plant species. These features aid botanists in more precisely recognizing major species of plants from leaf images. One of the most essential and complex aspects of leaf in plant species is the teeth. The leaf images are pre-processed in this study, and the segmentation is done using Canny edge detection. The features are extracted using a feature extraction model and CNN model namely fine-tuned VGG16. These features are then classified using the CNN model and acquired an average accuracy of 95.45 percent.

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things (IoT) technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real‐time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three‐axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.

A common way to solve the problem of how to obtain the optimal experimental conditions and the experimental results based on limited experimental data is that use of the traditional genetic algorithm optimization BP neural network algorithm (BPGA). However, the experimental results are often unstable and the error of the optimal results is also relatively large. Therefore, we propose a method of sinusoidal adaptive genetic algorithm (SAGA) double optimization BP neural network. The simulation results show that our proposed algorithm effectively improves the accuracy of the extreme value optimization of the nonlinear function. The network prediction error reaches -0.008 to 0.008, the average extreme value is accurate to 0.0084, and the optimal extreme value is accurate to 0.00007.