Flower Images Research Articles

An intelligent flower classification framework: optimal hybrid flower pattern extractor with adaptive dynamic ensemble transfer learning-based convolutional neural network

ABSTRACT The rapid development in computer technology plays an essential role in the research works for performing fast and accurate identification of flower species through the processing of flower images with the support of mobile devices that seeks more attention in the research areas. It is highly significant for maintaining the sensitivity of ecological balance, and therefore, the image-processing approaches have provided improved outcomes in recent days. To rectify these existing problems and improve the accuracy attains the flower classification, the CNN variants of ensemble techniques can be used for the dynamic ensemble selection of the CNN networks. This paper develops an enhanced ensemble deep learning-based flower classification model to get efficient classification results with optimal models. The pre-processing is done through the Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering techniques. The pre-processed images are considered for the optimal pattern extraction phase, where optimal hybrid patterns are extracted from the Local Binary Pattern (LBP) and Local Vector Pattern (LVP). Here, the optimal hybrid pattern extraction phase contains the optimization in it using the enhanced heuristic algorithm named Improved Rat Swarm Optimizer (IRSO). The flower classification is performed with an Adaptive Dynamic Ensemble Transfer learning-based Convolutional Neural Network (ADET-CNN). Here, the optimal models are selected with the support of the same RSO algorithm. The experimental results show a better efficacy of the developed flower classification framework.

꽃의 이미지를 활용한 업스타일 연구

Recently, the average age of marriage has gradually increased, and the micro-wedding has become more popular. Under these circumstances, this study is meaningful in that it aimed to examine the diversity of updo hairstyles in simplified wedding culture. In updo hair accessories, there has been an attempt to change from artificial jewelry to natural flowers. Even during such transition, the pursuit of beauty remains unchanged. As the richness and beauty of flowers in diverse colors and shapes make people happy, this study attempted to express beauty, using up-styling techniques and elements such as shape, texture and color with flower images as motives with a goal of making guests happy and satisfied. To meet a bride’s desire to remember her wedding moment forever and make her more beautiful with a flower-based updo style, this study created hairstyles with flowers as motives for a blessed wedding. Specifically, yellow chrysanthemum and green calla symbolize ‘wealth & authority’ and ‘love of thousand years’ respectively while pink Lamprocapnosand green rose represent ‘love in heat shape’ and ‘world of imagination’ each.

Flower surface is warmer in center than at edges in alpine plants: evidence from Qinghai-Tibetan Plateau

Abstract Although flower temperature plays an important role in plant reproduction, how it varies spatially on the flower surface is unclear, especially in alpine plants. To characterize spatial variation in flower surface temperature, we examined thermal images of flowers of 18 species along an altitudinal transect from 3200 to 4000 m on Lenglong Mountain on the north-eastern Qinghai-Tibetan Plateau. The surface temperature varied considerably within a flower or floral unit in all plants under sunlight, and was about 1 °C with a maximum of 11 °C higher in the center than at the edges. Solar radiation and flower shape significantly affected the temperature range and standard deviation and the ratio of flower center to edge temperature. The spatial variability of temperature increased with flower size. Flowers in the Asteraceae had higher surface temperatures, greater spatial variability of temperature, and consistently higher and more stable temperatures in the center than at the edge. The ratio of flower center to edge temperature increased with altitude in most species. Heat buildup at the flower center is likely to be widespread in alpine plants; further studies are needed to explore its ecological and evolutional roles.

Everyone’s an Artist

I had just finished a collage—images of flowers and sunrises and words cut out of magazines, glued in layers along with beads and glitter—to express how I felt that day. Then I showed it to someone close to me. “That looks like something a 5-year-old created.”

基于融合注意力机制的 YOLOv5 算法对梨花序的识别研究

Thinning is an important agronomic process in pear production, thus the detection of pear inflorescence is an important technology for intelligentization of blossom thinning. In this paper, images of buds and flowers were collected under different natural conditions for model training, and the images were augmented by data augmentation methods. Model training was performed based on the YOLOv5s network with coordinate attention mechanism added to the backbone network and compared with the native YOLOv5s, YOLOv3, SSD 300, and Faster-RCNN algorithms. The mAP, F1 score and recall of the algorithm reached 93.32%, 91.10%, and 91.99%. The model size only took up 14.1 MB, and the average detection time was 27 ms, which are suitable for application in actual intelligent blossom thinning equipment.

Generating micro/nanostructures on magnesium alloy surface using ultraprecision diamond surface texturing process

The lightness and high strength-to-weight ratio of the magnesium alloy have attracted more interest in various applications. However, micro/nanostructure generation on their surfaces remains a challenge due to the flammability and ignition. Motivated by this, this study proposed a machining process, named the ultraprecision diamond surface texturing process, to machine the micro/nanostructures on magnesium alloy surfaces. Experimental results showed the various microstructures and sawtooth-shaped nanostructures were successfully generated on the AZ31B magnesium alloy surfaces, demonstrating the effectiveness of this proposed machining process. Furthermore, sawtooth-shaped nanostructures had the function of inducing the optical effect and generating different colors on workpiece surfaces. The colorful letter and colorful flower image were clearly viewed on magnesium alloy surfaces. The corresponding cutting force, chip morphology, and tool wear were systematically investigated to understand the machining mechanism of micro/nanostructures on magnesium alloy surfaces. The proposed machining process can further improve the performances of the magnesium alloy and extend its functions to other fields, such as optics.

My favorite flowering image: Arabidopsis conical petal epidermal cells.

Journal Article Accepted manuscript My favorite flowering image: Arabidopsis conical petal epidermal cells Get access Vivian F Irish Vivian F Irish Department of Molecular, Cellular and Developmental Biology, Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA Correspondence: Vivian.irish@yale.edu Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of Experimental Botany, erad106, https://doi.org/10.1093/jxb/erad106 Published: 18 March 2023 Article history Received: 14 March 2023 Published: 18 March 2023

Using lightweight deep learning algorithm for real-time detection of apple flowers in natural environments

Flower thinning at the most appropriate stage could achieve high and stable yield of apple. Achieving the accurate and real-time detection of apple flowers can provide necessary technical support for the vision system of thinning robots. An apple flower detection method based on lightweight YOLOv5s algorithm was proposed. The original Backbone of YOLOv5s was replaced by ShuffleNetv2, and the Conv module of the Neck part of YOLOv5s network was replaced by Ghost module. ShuffleNetv2 reduced the memory access cost through Channel Split operation. Ghost module reduced the computing cost of the general volume layer while maintaining the similar detection performance. The combination of these two methods in the improvement of YOLOv5s network can greatly reduce the size of the model and improve the detection speed, which was convenient for the migration and application of the model. To verify the effectiveness of the model, 3005 apple flower images in different environments were used for training and testing. The Precision, Recall, and mean Average Precision (mAP) of YOLOv5s-ShuffleNetv2-Ghost model were 88.40 %, 86.10 %, and 91.80 %, respectively, the model size was only 0.61 MB, and the detection speed was 86.21 fps. The detection speed of YOLOv5s-ShuffleNetv2-Ghost model on the Jetson nano B01 development board was 2.48 fps. The results showed that the method was feasible for real-time and accurate detection of apple flowers. The research can provide technical reference for the development of orchard flower thinning robots.

Towards autonomous cross-pollination: Portable multi-classification system for in situ growth monitoring of tomato flowers

A fundamental step towards an autonomous cross-pollination process for tomato plants involves classifying tomato flowers in line with their maturity. This paper is concerned with the development of a novel, portable, and deep learning enabled multi-classification system for classifying maturity levels of tomato flowers. The multi-class classification is powered by individual tomato flower images. As a dataset with individual tomato flowers is currently unavailable commercially, a dataset with more than 2000 images was created ensuring diversity among maturity levels and several other factors. A Convolutional Neural Network (CNN) was trained and tested with this dataset to be integrated with a feasible technology to institute portability in the system. Given the common use of smartphones, an Android based mobile application was developed to enable portable access to the neural network. A user would be able to capture or insert a tomato flower image and receive the predicted level of maturity using the mobile application. Rigorous testing show that the portable system generates comprehensive predictions reflecting the 75% classification accuracy possessed by the neural network. This paper also highlights the introductory work leading to the above-mentioned initiatives, culminated with future work paving the way towards autonomous tomato flower cross-pollination.

Research on Flower Image Classification Based on Transfer Learning

Due to the high similarity between flowers, it is difficult to identify them if they do not have the corresponding biological knowledge when classifying varieties manually. Given the above problems, to improve the accuracy and efficiency of flower classification, this paper proposes a migration parameter pre-training and fine-tuning VGG16 model based on the ImageNet data set to solve this problem. In this paper, the grid coverage enhancement method enhances the flower classification data set to expand the training sample data. The model uses the migration learning pre-training and fine-tuning method to improve network stability and accelerate network convergence. The results of comparative experiments show that the performance of the improved model has been significantly improved, and the result is better on the flower image data set, which has specific practical value.

Strawberry yield monitoring based on a convolutional neural network using high-resolution aerial orthoimages

This article presents the results of studies comparing the quality of work of two modern models of convolutional neural networks YOLOv7 and YOLOv8 used to monitor the yield of strawberries. To do this, we used the transfer method of machine learning models on a set of collected data consisting of four classes of development of generative formations of strawberry. As a result of the study, we obtained a data set that contained images of flowers, ovaries, mature and not mature berries. To ensure the balance of classes in the dataset, the Oversampling method was used, which included the generation of new images by applying various operations, such as resizing the image, normalizing brightness and contrast, converting images by rotating them by a certain angle and reflection, random noise addition, Gaussian blur. To collect data (images) in the field, a DJI Phantom 2 quadrocopter with a DJI Zenmuse Gimbal suspension and a GoPro HD HERO3 camera was used. To assess the quality of the YOLOv7 and YOLOv8 models when recognizing specified classes, well-known metrics were used that estimate the proportion of objects found that are really objects of a given class, such as Precision, Recall and mAP. Analysis of the results showed that the mAP metric for all classes of the YOLOv7 convolutional neural network model was 0,6, and the YOLOv8 model was 0,762. Analysis of the test sample images showed that the average absolute percentage error of image recognition of all classes by the YOLOv7 and YOLOv8 models was 9,2%. The most difficult to recognize was class the ovary of strawberries, the average absolute percentage error of which was 13,2%. In further studies, the use of high-resolution stereo cameras is recommended, which will further improve the accuracy of monitoring potential yields due to the possibility of determining the dimensional parameters of strawberry fruits and constructing 3D models of elevation maps using photogrammetry.

Flower image classification based on an improved lightweight neural network with multi-scale feature fusion and attention mechanism.

In order to solve the problem that deep learning-based flower image classification methods lose more feature information in the early feature extraction process, and the model takes up more storage space, a new lightweight neural network model based on multi-scale feature fusion and attention mechanism is proposed in this paper. First, the AlexNet model is chosen as the basic framework. Second, a multi-scale feature fusion module (MFFM) is used to replace the shallow single-scale convolution. MFFM, which contains three depthwise separable convolution branches with different sizes, can fuse features with different scales and reduce the feature loss caused by single-scale convolution. Third, two layers of improved Inception module are first added to enhance the extraction of deep features, and a layer of hybrid attention module is added to strengthen the focus of the model on key information at a later stage. Finally, the flower image classification is completed using a combination of global average pooling and fully connected layers. The experimental results demonstrate that our lightweight model has fewer parameters, takes up less storage space and has higher classification accuracy than the baseline model, which helps to achieve more accurate flower image recognition on mobile devices.

ГОЛОС О. МАНДЕЛЬШТАМА В ТВОРЧЕСТВЕ В. ДУДАРЕВА: НАЦИОНАЛЬНЫЙ ОБРАЗ СМЕРТИ

The object of the article is the national image of death in Russian verbal artistic culture. The subject of the research is the thanatological text in Russian poetry. The material for the article is two poems - “Why is the soul so melodious” by O.E. Mandelstam and “There are quiet valleys in Russia…” by V.F. Dudarev. At the center of hermeneutic and culturalphilosophical analysis is the interaction of two types of reality, phenomenal and noumenal, rational and spontaneous in art. Much attention is paid to the image of death in these two poems and the images of wind, groves, and flowers associated with it. The research methodology is reduced to a holistic ontohermeneutic analysis of poetic texts by O.E. Mandelstam and V.F. Dudarev, aimed at highlighting the cultural potential of the problem of perception of the phenomenon of death in the Russian national image of the world, which allows penetrating the poetic word from ontological and apophatic positions, as well as revealing the significance of the literary traditions of the Silver Age in the work of a modern poet; typological and historical-literary methods of text research are applied. The results of the work may be of interest to philologists who include literature in the space of a large dialogue of cultures, and can also be used in teaching courses in cultural studies and Russian philosophy.

신자연주의 미학적 특성을 융합한 네일아트 디자인 연구

The purpose of this study is to present a neo-naturalistic nail art design that combines the aesthetic characteristics of digital with nail art techniques using natural images as a motif. Through a review of the preceding paper, neonaturalism was defined as a nail art design that combines digital characteristics with natural images of flowers, oceans, and universes. First, natural images of flowers, the sea, and the universe were reproduced with nail art designs (3 works). The neo-naturalist nail art design has produced 3 works that maximize the splendor of flowers, richness and beautiful images, the separation of sea and sand, and the non-static image of bubbles, and the various glitters and dreams of the universe. This study proposed a neo-naturalistic nail art design in which digital elements are fused to the natural image motif extracted from the nail art design that reproduces the appearance of nature. Through this, it is believed that the possibility of nail art design was discovered as a means of expressing the value of nature preservation, and the scope of use of nail art design was expanded.

An Optimized Flower Categorization Using Customized Deep Learning

Categorizing flowers is quite a challenging task as there is so much diversity in the species, and the images of the different flower species could be pretty similar. Flower categorization involves many issues like low resolution and noisy images, occluded images with the leaves and the stems of the plants and sometimes even with the insects. The traditional handcrafted features were used for extraction of the features and the machine learning algorithms were applied but with the advent of the deep neural networks. The focus of the researchers has inclined towards the use of the non-handcrafted features for the image categorization tasks because of their fast computation and efficiency. In this study, the images are pre-processed to enhance the key features and suppress the undesired information’s and the objects are localized in the image through the segmentation to extract the Region of Interest, detect the objects and perform feature extraction and the supervised classification of flowers into five categories: daisy, sunflower, dandelion, tulip and rose. First step involves the pre-processing of the images and the second step involves the feature extraction using the pre-trained models ResNet50, MobileNet, DenseNet169, InceptionV3 and VGG16 and finally the classification is done into five different categories of flowers. Ultimately, the results obtained from these proposed architectures are then analyzed and presented in the form of confusion matrices. In this study, the CNN model has been proposed to evaluate the performance of categorization of flower images, and then data augmentation is applied to the images to address the problem of overfitting. The pre-trained models ResNet50, MobileNet, DenseNet169, InceptionV3 and VGG16 are implemented on the flower dataset to perform categorization tasks. The pre-trained models are empirically implemented and assessed on the various flower datasets. Performance analysis has been done in terms of the training, validation accuracy, validation loss and training loss. The empirical assessment of these pre-trained models demonstrate that these models are quite effective for the categorization tasks. According to the performance analysis, the VGG16 outperforms all the other models and provides a training accuracy of 99.01%. Densenet169 and MobileNet also give comparable validation accuracy. ResNet50 gives the lowest training accuracy of 60.46% as compared with the rest of the pre-trained replica or models.

Deep learning-based plant classification and crop disease classification by thermal camera

Studies regarding image classification based on plant and crop disease images that were acquired using a visible light camera have been conducted in the past, whereas those based on thermal images are limited. This is because the thermal images are blurry due to the nature of the thermal camera, which makes it extremely difficult to classify objects. Therefore, this study proposes a new plant and crop disease classification method based on thermal images. The proposed method used a convolutional neural network with explainable artificial intelligence (XAI) to improve plant and crop disease classification performance.A new thermal plant image dataset was built for conducting the experiments, which contained 4,720 various images of flowers and leaves. In addition, an open database of crop diseases was also used, such as the Paddy crop dataset. The proposed plant and crop disease classification method demonstrated a 98.55% accuracy for the thermal plant image dataset and a 90.04% accuracy for the Paddy crop dataset, both of which outperformed other existing methods.

Machine learning methods for efficient and automated in situ monitoring of peach flowering phenology

Accurate knowledge of peach flowering phenology is essential for scheduling precise irrigation and managing artificial pollination for breeding. However, in situ monitoring of peach flowering phenology is laborious and challenging. This study divided peach flowering phenology into eight stages covering inflorescence emergence and flowering using BBCH scales. Web-connected recording cameras and weather stations automatically-collected flower images and meteorological data from three peach cultivars. Daily variables, including area proportion of specific color ranges in the images, temperature, illuminance and heat/chilling requirement data were applied for training and testing four machine learning models: random forest (RF), support vector machine (SVM), naïve Bayes (NB), and k-Nearest Neighbors (KNN). Grid tuning and 10-fold cross-validations were conducted on these models to determine the optimal method for identifying peach flowering phenological stages. The RF model obtained highest optimal F1 score of 98.82 % (harmonic average of model accuracy and recall) among the models on the testing set. The area proportion of specific color ranges in the real-time images are essential for model performance. Heat requirement data (biological growth time and growing degree days) can improve the overall accuracy. The classification accuracy at different BBCH phenological stages of the three cultivars is acceptable for real-time monitoring of peach flowering phenology. These are applicable for peach breeding, heat stress management, and irrigation scheduling. The RF model also shows acceptable accuracy with flower images and public meteorological data input, which reveals the extendibility for phenology monitoring of other trees or crops.

From Secret Fashion Shoots to the #100projectors

From Secret Fashion Shoots to the #100projectors

REBELLIOUS FLOWERS IN THE GARDEN OF KATRI VALA

The article is devoted to the images of flowers, gardens and Chinese literary reminiscences in the work of the Finnish poetess Katri Vala, one of the most prominent members of the literary group «Tulenkantajat» («The Flame Bearers»). The purpose of the work is to explore for the first time in Russian literary criticism the literary images in the poetry of Katri Vala in relation to the Chinese poetry throughout the entire career of the poetess. The subject of study is the lyrics of Katri Vala, which contains an alternation of poetic landscapes, sometimes overlapping, and sometimes sharply contrasting. Using the comparative typological and the historical-literary methods Vala’s poems and translations are analyzed in the context of the convergence of Chinese and Finnish cultural traditions in two similar historical situations. The topic of the article is relevant both from a literary and historical points of view. Katri Vala was accused of aestheticism, of a love of the exotic. The author argues with such point of view, and contends that in this way the Vala’s deep awareness of self-involvement in the tradition of the Tang era Chinese poetry was manifested. This was presented in Vala’s works not only in the form of «ascension to flowers». The poetess was united with the great Tang poets by her civic position, sympathy for the common people, the formulation of social problems, and an appeal to the theme of humanism. The article introduces into scientific use letters stored in the archives of the Finnish Literary Society, articles from Finnish periodicals of the 1920s, which all testify to Katri Vala’s thorough knowledge of Chinese poetry. Convincing examples prove that the translations made by Katri Vala give the Finnish readers a complete picture of the lyrics of Li Bo and Du Fu. In all situations, Katri Vala’s poetry was optimistic, and literary Chinese reminiscences were forever preserved in it.

학습된 머신러닝의 표류 현상에 관한 고찰

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.