Статті в журналах: "Flower machine"

1

Rajkumar, D. "IRIS Species Predictor." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1530–35. http://dx.doi.org/10.22214/ijraset.2022.40097.

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Abstract: In Machine Learning, we are using semi-automated extraction of knowledge of data for identifying IRIS flower species. Classification is a supervised learning in which the response is categorical that is its values are in finite unordered set. To simply the problem of classification, scikit learn tools have been used. This paper focuses on IRIS flower classification using Machine Learning with scikit tools. Here the problem concerns the identification of IRIS flower species on the basis of flowers attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS flower and how the prediction was made from analyzing the pattern to from the class of IRIS flower. In this paper we train the machine learning model with data and when unseen data is discovered the predictive model predicts the species using what it has been learnt from the trained data. Keywords: MATLAB, Machine learning, Neural Network.

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Vanalkar, Prof A. V., Mahesh Dhodre, Pratik Anwane, Prajwal Mantinwar, Ritesh Kanade, Shivam Kalbande, and Aniket Mohite. "Design and Fabrication of Marigold Flower Hydrosol Extraction Machine and Manure Making." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1221–23. http://dx.doi.org/10.22214/ijraset.2022.42500.

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Abstract: India is the religious country with a huge population. And their are lots of hindu temple present here. This devotion towards god comes with price in the from of flower. And this flower create a lot of pollution in solid from and due to improper management of this waste create air pollution and other types of pollution which harms the nature critically. In an average in India their are 60 cores tons of marigold flower are devoted to the god and create such a problems as we discuss. Hence to tackle such a situation in such a way to create some revenue from those waste and also reduce pollution we design and fabricated marigold flowers hydrosol and manure extracting machine. This machine works on water distillation process and represent thermodynamic cycle. Here flowers get boiled with water and it create steam of flower essences and get cold down into liquid from in condenser and by this process we get hydrosol and manure (in boiler). Their are also other process to extract these items from flower but water distillation process is more easy, effective and simple than other process and it create absolutely low or no pollution

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3

Kaur, Rupinder, Dr Anubha Jain, Pushpanjali Saini, and Sarvesh Kumar. "A Review Analysis Techniques of Flower Classification Based on Machine Learning Algorithms." ECS Transactions 107, no. 1 (April 24, 2022): 9609–14. http://dx.doi.org/10.1149/10701.9609ecst.

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A system design for the recognition of flower species will be very beneficial to only in agriculture but also in industries of pharmaceutical science, study, and practice botany, trade, and farming. For the flower classification system, it will require extra species testing as there are varieties of species in flowers and thus it become exceptionally difficult to characterize them when it comes for the basic identification of flower among same species. Therefore, this subject has already become crucial for research purposes. So, for better detection, various techniques have been implemented through machine learning, which the latest trends become for such problems solving. Machine learning is well known for the strongest for its large part of classification and recognition performance in the computer. Classification is the most vital approaches of AI. Major brief of machine learning is information evaluation. Numerous algorithms present for system classification like decision trees (DT), Neural Network, Navie Bayes, SVM, Back Propagation, multi-class classification, Artificial Neural, K-nearest neighbor, multi-layer perception, etc. The paper summarizes the foremost aspects of machine learning and its drawbacks.

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4

Gast, Karen L. B. "Evaluation of Stem and Flower Strength of Different Freeze-dried Peony Cultivars." HortScience 31, no. 4 (August 1996): 637c—637. http://dx.doi.org/10.21273/hortsci.31.4.637c.

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The floral industry is always trying to identify new products for consumers. Dried/preserved products have gained in popularity because they have a long vase life and offer a wide range of forms, textures and shapes. Among these new dried/preserved products are freeze dried flowers. Freeze drying preserves flower color and shape better than air and matrix drying. From a grower's standpoint, they need to know which plants and which of their cultivars will freeze dry better than others, especially if the plant is a perennial that takes time to come into production. Peonies are a good example. Fragility of the flowers after freeze drying is one of the most important factors determining the suitability of a plant and its cultivars. The objective of this study was to evaluate the flower and stem strength of freeze dried peony flowers of several cultivars to be able to recommend to growers which cultivars freeze dried better than others. Flowers from different red, pink and white herbaceous peony cultivars were freeze dried using commercial equipment and protocols. Stem and flower strengths were determined by compression tests via an Instron Universal Testing Machine. There were no differences in flower strength among the white cultivars evaluated. Flowers of the red cultivars, `Shawnee Chief', `David Harum', `Kansas', and `Monsieur Martin Cahuzac', were stronger than most of the other reds evaluated. `James Pillow' flowers were stronger than most other pinks. There were no differences in flower strength among the other pink cultivars. Lack of differences in flower and stem strength provides growers with a wider selection of suitable cultivars.

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5

Patel, Alakh, and Jaymin Bhalani. "Development of Cotton Flower Picking Machine based on Machine Vision Technique." International Journal of Computer Applications 180, no. 42 (May 17, 2018): 22–26. http://dx.doi.org/10.5120/ijca2018917114.

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6

Ornai, Alon, and Tamar Keasar. "Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web." Plants 9, no. 11 (October 24, 2020): 1432. http://dx.doi.org/10.3390/plants9111432.

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Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes as a key shape trait that explains the composition of their animal visitors. Yet, additional shape-related parameters, related to the handling difficulty of flowers, may be important as well. We analyzed a dataset of 2288 visits by six bee genera to 53 flowering species in a Mediterranean plant community. We characterized the plant species by five discrete shape parameters, which potentially affect their accessibility to insects: floral shape class, tube depth, symmetry, corolla segmentation and type of reproductive unit. We then trained a random forest machine-learning model to predict visitor identities, based on the shape traits. The model’s predictor variables also included the Julian date on which each bee visit was observed and the year of observation, as proxies for within- and between-season variation in flower and bee abundance. The model attained a classification accuracy of 0.86 (AUC = 0.96). Using only shape parameters as predictors reduced its classification accuracy to 0.76 (AUC = 0.86), while using only the date and year variables resulted in a prediction accuracy of 0.69 (AUC = 0.80). Among the shape-related variables considered, flower shape class was the most important predictor of visitor identity in a logistic regression model. Our study demonstrates the power of machine-learning algorithms for understanding pollination interactions in a species-rich plant community, based on multiple features of flower morphology.

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7

Sumiati, Ruzita, Genta Ramadeto, Rakiman Rakiman, and Fardinal Fardinal. "Pembuatan Dan Pengujian Mesin Bending Rotary Baja Untuk Aplikasi Stand Pot Bunga Diameter 8 dan 10 Inch." Jurnal Teknik Mesin 13, no. 1 (June 30, 2020): 13–17. http://dx.doi.org/10.30630/jtm.13.1.363.

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In this day and age people decorate homes with flower pots and many models of flower pot place creations. Usually the place to put flower pots in the form of a circle and to make a circular iron is made manually requires energy and a long time. A bending machine is needed to make concrete iron circle creations. The aim of this research is to produce a rotary bending machine that is able to work efficiently in forming a concrete steel circle. The method used in this study is a practical method that is doing machine design and continued with the manufacture and field testing. The working principle of this machine is to use a motor that functions to move the Gearbox and continue the rotation to the shaft, then the shaft rotates and the bending mall rotates and makes the steel concrete come round and circular. The conclusion is (a). This concrete steel rotary bending machine has dimensions of 550 mm x 550 mm x 1000 mm with an electric motor drive power source (b). The results of the bending process using a concrete steel rotary bending machine are far more efficient than doing manual bending

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8

Assirelli, Alberto, Daniela Giovannini, Mattia Cacchi, Sandro Sirri, Gianluca Baruzzi, and Giuseppina Caracciolo. "Evaluation of a New Machine for Flower and Fruit Thinning in Stone Fruits." Sustainability 10, no. 11 (November 7, 2018): 4088. http://dx.doi.org/10.3390/su10114088.

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Peach and apricot trees usually set more fruit than they can adequately support. Crop load adjustment through fruit thinning is a routine practice adopted by fruit growers to obtain a marketable product. However, hand thinning is an expensive, labor-intense operation. The interest in the mechanization of thinning has increased in the last decades. A new machine, consisting of a tractor-mounted rotor equipped with elastic rods radially inserted on a central axis, has been recently developed to thin both flowers and green fruits in stone fruit crops. In order to test its effectiveness and optimize the operative conditions, trials were carried out in 2016 in two apricot and two peach commercial orchards located in the northeast Italy. Tests were carried out on narrow-canopied orchards, during blooming time, and on green fruit, assessing the flower and fruit removal percentage and the labor saving as compared with the standard fruit hand-thinning practice. In apricot, the machine removed 20.8% of flowers and 43.6% of fruit, allowing 48% time saving in the follow-up fruit manual thinning as compared with the control (hand-thinning only). In peach, mechanical thinning at blooming time removed 63% of flowers, allowing 42.4% time saving in the follow-up fruit manual thinning as compared with the control, whereas mechanical thinning of fruit at the beginning of pit hardening stage removed less than 10%. The development of a mechanical thinning practice, complemented by a manual finishing, could represent a valuable near-term solution to reduce thinning labor time.

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9

Deora, Tanvi, Mahad A. Ahmed, Bingni W. Brunton, and Thomas L. Daniel. "Learning to feed in the dark: how light level influences feeding in the hawkmoth Manduca sexta." Biology Letters 17, no. 9 (September 2021): 20210320. http://dx.doi.org/10.1098/rsbl.2021.0320.

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Nocturnal insects like moths are essential for pollination, providing resilience to the diurnal pollination networks. Moths use both vision and mechanosensation to locate the nectary opening in the flowers with their proboscis. However, increased light levels due to artificial light at night (ALAN) pose a serious threat to nocturnal insects. Here, we examined how light levels influence the efficacy by which the crepuscular hawkmoth Manduca sexta locates the nectary. We used three-dimensional-printed artificial flowers fitted with motion sensors in the nectary and machine vision to track the motion of hovering moths under two light levels: 0.1 lux (moonlight) and 50 lux (dawn/dusk). We found that moths in higher light conditions took significantly longer to find the nectary, even with repeated visits to the same flower. In addition to taking longer, moths in higher light conditions hovered further from the flower during feeding. Increased light levels adversely affect learning and motor control in these animals.

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10

Kondo, Naoshi, Mitsuji Monta, Tanjuro Goto, and Haruhiko Murase. "Machine vision based quality evaluation of chrysanthemum cut flower." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5617–21. http://dx.doi.org/10.1016/s1474-6670(17)56958-x.

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11

Hari Krishna, Yaram, Kanagala Bharath Kumar, Dasari Maharshi, and J. Amudhavel. "Image Processing and Restriction of Video Downloads Using Cloud." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 327. http://dx.doi.org/10.14419/ijet.v7i2.32.15705.

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Flower image classification using deep learning and convolutional neural network (CNN) based on machine learning in Tensor flow. Tensor flow IDE is used to implement machine learning algorithms. Flower image processing is based on supervised learning which detects the parameters of image. Parameters of the image were compared by decision algorithms. These images are classified by neurons in convolutional neural network. Video processing based on machine learning is used in restriction of downloading the videos by preventing the second response from the server and enabling the debugging of the video by removing the request from the user.

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12

Zhang, Chongyuan, Wilson Craine, Rebecca McGee, George Vandemark, James Davis, Jack Brown, Scot Hulbert, and Sindhuja Sankaran. "Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops." Sensors 20, no. 5 (March 6, 2020): 1450. http://dx.doi.org/10.3390/s20051450.

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The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.

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13

Feng, Chiang Ling. "The Mathematical Analysis and Classification Research of an Iris Data Set Using Binary Tree and Grey Relation Grade." Journal of Physics: Conference Series 2068, no. 1 (October 1, 2021): 012004. http://dx.doi.org/10.1088/1742-6596/2068/1/012004.

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Abstract The data from an Iris flower database is studied. The Iris database is the most commonly used database for machine learning algorithms. The Iris database was developed by Ronald Aylmer Fisher in 1936. The Iris database has 150 records in three categories: Iris Sentosa, Iris Versicolor and Iris Virginic. The database has four attributes: sepal length, sepal width, petal length and petal width. For the machine learning algorithm, 150 Iris flower databases are used. Of the 150 Iris in the Iris database, 80% are used as the training set and the remaining 20% Iris as the test set. In machine learning, to perform classification and discrimination is a complicated and difficult thing. In this study, a grey relation grade is used to extract the main features of the Iris flower and a Binary Tree [1] is used to classify the Irises. The results show that for the same specific attributes, grey relation grade extracts the main attributes and can be used in combination with a binary for classification.

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14

Rasyid, Rachmat, and Abdul Ibrahim. "Implementation of Machine Learning Using the Convolution Neural Network Method for Aglaonema Interest Classification." Jurnal E-Komtek (Elektro-Komputer-Teknik) 5, no. 1 (June 28, 2021): 21–30. http://dx.doi.org/10.37339/e-komtek.v5i1.434.

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One of the wealth of the Indonesian nation is the many types of ornamental plants. Ornamental plants, for example, the Aglaonema flower, which is much favored by hobbyists of ornamental plants, from homemakers, is a problem to distinguish between types of aglaonema ornamental plants with other ornamental plants. So the authors try to research with the latest technology using a deep learning convolutional neural network method. It is for calcifying aglaonema interest. This research is based on having fascinating leaves and colors. With the study results using the CNN method, the products of aglaonema flowers of Adelia, Legacy, Widuri, RedKochin, Tiara with moderate accuracy value are 56%. In contrast, the aglaonema type Sumatra, RedRuby, has the most accuracy a high of 61%.

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15

Liu, Ting, Qinwei Fan, Qian Kang, and Lei Niu. "Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization." Processes 8, no. 12 (December 1, 2020): 1583. http://dx.doi.org/10.3390/pr8121583.

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Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.

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16

Arafa, G. K. "MODIFICATION OF A LOCAL MACHINE THRESHING UNIT FOR THRESHING SUN FLOWER." Misr Journal of Agricultural Engineering 30, no. 4 (October 1, 2013): 1007–22. http://dx.doi.org/10.21608/mjae.2013.99573.

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17

Lonsbary, Sheryl K., John O'Sullivan, and Clarence J. Swanton. "Reduced Tillage Alternatives for Machine-harvested Cucumbers." HortScience 39, no. 5 (August 2004): 991–95. http://dx.doi.org/10.21273/hortsci.39.5.991.

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Cucumber (Cucumis sativus L.) is grown using intensive tillage practices, which increase the cost of production and may lead to an increase in soil and water erosion. Research on alternative tillage practices for cucumber production has been limited primarily to exploring the benefits of no tillage. Alternative tillage practices, such as disking (one pass with a tandem disk) and zone tillage (one pass with a Trans-till) have not been investigated. Thus, the objective of this study was to compare the influence of reduced tillage practices on the growth, development, and yield of cucumbers. Seedling emergence varied between years, but was unaffected by a reduction in tillage, while cucumber leaf number, leaf area index, and vine growth were reduced by no tillage (P ≤ 0.05). Total dry matter accumulation and days to 50% open flower varied with tillage. No-tillage plots produced an average of 34 g·m-2 of dry matter compared to 47 g·m-2 for conventional tillage plots and took 1 day longer to reach 50% flower. Although growth differences were observed under all reduced tillage treatments, no reduction in total yield was observed when compared with conventional tillage yields. Alternative reduced tillage practices, such as disking or zone tillage, were found to be viable options for successful cucumber production. These alternative practices will reduce the cost of production, provide growers with greater time flexibility and ease of land preparation, and reduce the potential for water and wind erosion.

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18

Mccloy, B. L., and S. R. White. "The key to successful second year white clover seed crops." NZGA: Research and Practice Series 6 (January 1, 1996): 40. http://dx.doi.org/10.33584/rps.6.1995.3382.

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In recent years there has been an increase in the number of white clover seed crops taken for a second harvest. Traditionally yields from second year crops are substantially lower than first year crops. A trial was established investigating techniques to increase yields in second year crops. The trial was located in a dryland crop of white clover (cv. Grasslands Demand) 4 km east of Methven, mid Canterbury. It involved 10 herbicide treatments and 8 'inter-row' treatments arranged in a split block design. Number of mature flower heads were recorded on all treatments at harvest as an indirect estimate of yield. Additionally, selected treatments were cut and collected using a rotary type mower, threshed, and machine dressed for direct estimates of seed yield. Flower number and machine dressed seed yield were significantly (P

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19

V.D. Prasad, M., B. JwalaLakshmamma, A. Hari Chandana, K. Komali, M. V.N. Manoja, P. Rajesh Kumar, Ch Raghava Prasad, Syed Inthiyaz, and P. Sasi Kiran. "An efficient classification of flower images with convolutional neural networks." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 384. http://dx.doi.org/10.14419/ijet.v7i1.1.9857.

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Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

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20

Luo, Feng, and Ke Qiu. "Design of Cotton-Picking Laptop Machine without Winding." Key Engineering Materials 467-469 (February 2011): 593–96. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.593.

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The cotton fiber winding has always been a problem for a picking cotton machine to conquer. This design theoretically pointed out the reasons, at the same time around launched a mechanical cotton-pickers laptop without transmission mechanism, but with a flexible helicopter. This machine is made of cotton picking a direct drive of micro-motors to rotate, the protection cover to picking cotton, and gear transmission by the wind, thus instantly complete cotton flower of picking cotton, improve efficiency, but also greatly reduce the labor intensity of cotton.

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21

Giri, Santosh. "Image based flower species classification using CNN." Journal of Innovations in Engineering Education 2, no. 1 (March 1, 2019): 182–86. http://dx.doi.org/10.3126/jiee.v2i1.36670.

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Deep learning is one of the essential parts of machine learning. Applications such as image classification, text recognition, object detection etc. used deep learning architectures. In this paper neural network model was designed for image classification. A NN classifier with one fully connected layer and one softmax layer was designed and feature extraction part of inception v3 model was reused to calculate the feature value of each images. And by using these feature values the NN classifier was trained. By adopting transfer learning mechanism NN classifier was trained with 17 classes of oxford 17 flower image dataset. The system provided final training accuracy of 99 %. After training, system was evaluated with testing dataset images. The mean testing accuracy was 86.4%.

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S.Sarkate, Rajesh, and Prakash B. Khanale. "Domain Specific Knowledge based Machine Learning for Flower Classification using Soft Computing." International Journal of Computer Applications 104, no. 1 (October 18, 2014): 14–17. http://dx.doi.org/10.5120/18166-9027.

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23

Jeet, Kawal, Renu Dhir, and Sameer Sharma. "Bi-criteria parallel machine scheduling using nature-inspired hybrid flower pollination algorithm." International Journal of Metaheuristics 5, no. 3/4 (2016): 226. http://dx.doi.org/10.1504/ijmheur.2016.081153.

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Sharma, Sameer, Renu Dhir, and Kawal Jeet. "Bi-criteria parallel machine scheduling using nature-inspired hybrid flower pollination algorithm." International Journal of Metaheuristics 5, no. 3/4 (2016): 226. http://dx.doi.org/10.1504/ijmheur.2016.10002048.

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Taha Chicho, Bahzad, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, and Dilovan Assad Zebari. "Machine Learning Classifiers Based Classification For IRIS Recognition." Qubahan Academic Journal 1, no. 2 (May 4, 2021): 106–18. http://dx.doi.org/10.48161/qaj.v1n2a48.

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Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.

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Umamaheswara Raju, R. S., R. Ramesh, V. Ramachandra Raju, and Sharfuddin Mohammad. "Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation." Journal of Optics 47, no. 2 (March 8, 2018): 243–50. http://dx.doi.org/10.1007/s12596-018-0457-y.

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Mejahed, Sara, and M. Elshrkawey. "A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization." PeerJ Computer Science 8 (January 12, 2022): e834. http://dx.doi.org/10.7717/peerj-cs.834.

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The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.

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Petrich, Lukas, Georg Lohrmann, Matthias Neumann, Fabio Martin, Andreas Frey, Albert Stoll, and Volker Schmidt. "Detection of Colchicum autumnale in drone images, using a machine-learning approach." Precision Agriculture 21, no. 6 (May 6, 2020): 1291–303. http://dx.doi.org/10.1007/s11119-020-09721-7.

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Abstract Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites.

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Kang, Xue Juan, Jun Feng Jing, Jia Kun Li, and Qing He. "Application Design of CAN Bus (CANOpen) for Rotary Screen Printing Machine." Applied Mechanics and Materials 84-85 (August 2011): 447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.84-85.447.

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The background of this paper is Rotary Screen Printing Machine(RSPM),and the paper need to resolve the printing flower dislocation phenomenon caused by synchronization between each motor, and by the synchronization between rotary screen motor and the conduction band. The paper presented overall scheme, and analyzed, designed and realized synchronous control arithmetic design based on system hardware platform. Through some related experimental results proved that the distributed system based on CAN bus(CANOpen protocol) worked stably, each motor transmitted independently and could run synchronously , and the results verified the correctness and feasibility of distributed structure scheme used for RSPM system.

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Indrie, Liliana, Pablo Diaz-García, and Zlatina Kazlacheva. "THE USE OF CAD/CAM FOR TEXTILE DESIGNS AND FABRICS." Applied Researches in Technics, Technologies and Education 7, no. 1 (2019): 24–28. http://dx.doi.org/10.15547/artte.2019.01.003.

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The paper illustrates the role of CAD/CAM technology in developing virtual textile design on the computer screen. First, it was created the shape of the symbol "Flower of Life" from Sacred Geometry by using the CAD system ASCON Kompas for the design of shapes and the graphic software Adobe Photoshop for colour combinations. Then, we drew few graphic sketches of garments on which we applied the textile design “Flower of life”. In order to obtain fabric-manufacturing orders, the image was processed with a specific design software for fabric: EAT DesignScope Victor. At last, we converted the design into a fabric by using the weaving machine Smit Textile GS900 Jacquard Loom.

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Venkateswarlu, S., Janaki M, and Thirumalaivasan R. "Design of Power System Stabilizer using Flower Pollination Algorithm." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 177. http://dx.doi.org/10.14419/ijet.v7i4.10.20831.

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The Power System Stabilizer (PSS) is a controller which is used to mitigate the instability of Low Frequency Oscillations (LFOs) in power systems. The condition of oscillatory instability can also cause the loss of generator synchronism. It is observed that the damping provided by PSS depends on the proper selection of its parameters. This paper presents the systematic method for the selection of PSS parameters using evolutionary nature inspired optimization technique called Flower Pollination Algorithm (FPA). FPA is employed for selecting the optimal parameters of PSS so as to mitigate the low frequency oscillations of generator rotor and thereby oscillatory instability. The system consists of Single Machine with PSS which is connected to Infinite Bus (SMIB) through a transmission line. The transient simulation validates the performance of the system with optimized PSS. The results show that PSS with FPA optimized parameters provides fast and stable response.

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Bante, Rupesh, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, and Sofia Pillai. "Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color." International Journal of Computer Sciences and Engineering 7, no. 3 (March 31, 2019): 1053–58. http://dx.doi.org/10.26438/ijcse/v7i3.10531058.

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Reddy, N. Sivarami, D. V. Ramamurthy, M. Padma Lalitha, and K. Prahlada Rao. "Minimizing the total completion time on a multi-machine FMS using flower pollination algorithm." Soft Computing 26, no. 3 (October 27, 2021): 1437–58. http://dx.doi.org/10.1007/s00500-021-06411-y.

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Warinussy, Rachel Priskila Louwrensya, Dewi Kristiana, and FX Ady Soesetijo. "Pengaruh Perendaman Nilon Termoplastik Dalam Berbagai Konsentrasi Ekstrak Bunga Cengkeh Terhadap Modulus Elastisitas (The Effect of Thermoplastic Nylon Immersion In Various Concentration of Clove Flower Extract to the Modulus Elasticity)." Pustaka Kesehatan 6, no. 1 (January 17, 2018): 179. http://dx.doi.org/10.19184/pk.v6i1.7155.

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Background:Thermoplastic nylon is one of denture basic alternative made by resin polyamide because its high flexibility and good translutient, but hard to be smoothed and polished causing food waste accumulation and plaque formation. Clove flower extract proved as antiseptic used to be a denture cleanser material, but its contens of phenol essence can break the thermoplastic nylon chain. Purpose: The aim of this study was to determine the effect of thermoplastic nylon immersion in various concentration: 0,8%, 1%, 1,2%, 1,4% and 1,6%, of clove flower extract to the modulus elasticity. Materials and Methods: This study was an laboratories experimental using post-test only control group design. The samples in 65 mm x 10 mm x 2,5 mm size were 30 samples. Those samples grouped into 6 groups immersed in aquadest and clove flower extract solution that is grouped based on the concentrations: 0,8%, 1%, 1,2%, 1,4% and 1,6% for 23 days. The modulus elasticity of thermoplastic nylon measured with Universal Testing Machine (UTM). Data was analysed using One Way Anova. Result and Conclusions: the conclusion of this study represented that thermoplastic nylon plates immersion in clove flower extract at the concentration 0,8%, 1%, 1,2%, 1,4% and 1,6% was affected the modulus elasticitys. The most effective clove extract concentration to be use as denture cleanser was 1,6% because it has the lowest increasing value of nylon thermoplastic modulus elasticity. Keyword: Clove flower extract, modulus elasticity, thermoplastic nylon.

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Parent, Serge-Étienne, Jean Lafond, Maxime C. Paré, Léon Etienne Parent, and Noura Ziadi. "Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem." Plants 9, no. 10 (October 21, 2020): 1401. http://dx.doi.org/10.3390/plants9101401.

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Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale.

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Aguilar-Mejía, Omar, Hertwin Minor-Popocatl, and Ruben Tapia-Olvera. "Comparison and Ranking of Metaheuristic Techniques for Optimization of PI Controllers in a Machine Drive System." Applied Sciences 10, no. 18 (September 21, 2020): 6592. http://dx.doi.org/10.3390/app10186592.

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Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a complex behavior with a nonlinear representation, but a disadvantage is the procedure to find the optimal PI controller gains. The optimal values of PI parameters must be computed during the tuning process. However, traditional tuning techniques are based on model and do not provide optimal adjustment parameters for the PI controllers because the transient response could produce oscillations and a large overshoot. In this paper, six swarm intelligence-based algorithms (whale, moth-flame, flower pollination, dragonfly, cuckoo search, and modified flower pollination), are correctly conditioned and delimited to tune the PI controllers, the results are probed in a typical industry actuator. Also, a rigorous study is developed to evaluate the quality and reliability of these algorithms by a statistical analysis based on non-parametric test and post-hoc test. Finally, with the obtained results, some time simulations are carried out to corroborate that the nonlinear system performance is improved for high precision industrial applications subjected to endogenous and exogenous uncertainties in a wide range of operating conditions.

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37

Kang, Di, Youhua Fan, Zejun Chen, Fang Ma, Shaofeng Peng, and YongWang. "Camellia Oleifera Harvester Based on Model Processing." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012112. http://dx.doi.org/10.1088/1742-6596/2066/1/012112.

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Abstract Aiming at the problems of high leakage rate and high damage rate in mechanized picking of Camellia oleifera fruit, a branch shaking type picking machine was designed. According to the working principle of picking, the structure design of key components was completed, and the orthogonal test of three factors and three levels was designed. The results showed that the best combination of operating parameters was: picking time 45s, vibration frequency 8Hz, amplitude 5cm of picking head. The field test of Camellia oleifera fruit picking machine was carried out, and the picking rate was 91.2%, and the damage rate of flower bud was 18.2%.

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Gupta, Himanshu, and Roop Pahuja. "Estimating Morphological Features of Plant Growth Using Machine Vision." International Journal of Agricultural and Environmental Information Systems 10, no. 3 (July 2019): 30–53. http://dx.doi.org/10.4018/ijaeis.2019070103.

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Motivated by the fact that human visionary intelligence plays a vital role in guiding many of the agriculture practices, this article represents an effective use of machine vision technology for estimating plant morphological features to ascertain its growth and health conditions. An alternative to traditional, manual and time-consuming testing methods of plant growth parameters, a novel online plant vision system is proposed and developed on the platform of virtual instrumentation. Deployed in real time, the system acquires plant images using digital camera and communicates the raw image to host PC on Wi-Fi network. The dedicated application software with plant user interface, effective image processing and analysis algorithms, loads the plant images, extracts and estimates certain morphological features of the plant such as plant height, leaf area, detection of flower onset and fall foliage. The system was tested and validated under real-time conditions using different plants and leaves. Further, the performance of the system was statistically analysed to show promising results.

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Wang, Li Fen. "Greenhouse Environment Monitoring System Design and Implementation." Advanced Materials Research 1079-1080 (December 2014): 451–55. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.451.

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the supervising system of flower house's enviroment,which is put into function by the popular series of AT89S52 single machine tablet,mainly measures and controls the major tempreture and humidity of enviroment,which will be revealed by showing system.the paper gives an introduction of system's software,hardware design and the process of performing.the system is easily manured with great utility.at the same time, it worths being popularized and used.

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40

Lv, W. G., J. Bai, and Ce Zhang. "Design of Carpet-Knitting Machine Control System Based on Embedded System." Applied Mechanics and Materials 743 (March 2015): 239–43. http://dx.doi.org/10.4028/www.scientific.net/amm.743.239.

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Electronic jacquard machine is the main knitting equipment now, and its control system is the device’s core component that can influence working performance. This paper introduces design and implementation of the Electronic Jacquard Control System for the warp woof knitting machine, and it is a high performance and scalable control system. The hardware of control system is designed with modular hierarchical method. The system contains hardware buffer and ensure reliable operation in real time, can continue weaving after outages, while, with sufficient flexibility and maintainability, can be adapted to different needs of different grades of carpet jacquard equipment. Control system supports a variety of pattern data input, providing flower pattern compilation features real-time visualization of dynamic control of the weaving process to improve the efficiency of the weaving machine. Control system based on general hardware and software platform: ARM9 and Embedded Linux operating system.

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41

Shah, Vishal, and Neha Sajnani. "Multi-Class Image Classification using CNN and Tflite." International Journal of Research in Engineering, Science and Management 3, no. 11 (November 20, 2020): 65–68. http://dx.doi.org/10.47607/ijresm.2020.375.

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In recent years’ machine learning is playing a vital role in our everyday lifelike, it can help us to route somewhere, find something for what we aren’t aware of, or can schedule appointments in seconds. Looking at the other side of the coin besides machine learning Mobile phones are equivocating and competing in the same field. If we take an optimistic view, by applying machine learning in our mobile devices, we can make our lives better and even move society forward. Image Classification is the most common and trending topic of machine learning. Among several different types of models in deep learning, Convolutional Neural Networks (CNN’s) have intimated high performance on image classification which are made out of various handling layers to gain proficiency with the portrayals of information with numerous unique levels, are the best AI models as of late. Here, we have trained a simple CNN and completed the experiments on the dataset called Fashion Mnist and Flower Recognition, and also analyzed the techniques of integrating the trained model in the Android platform.

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Wei, Pengliang, Ting Jiang, Huaiyue Peng, Hongwei Jin, Han Sun, Dengfeng Chai, and Jingfeng Huang. "Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images." Plant Phenomics 2020 (October 6, 2020): 1–15. http://dx.doi.org/10.34133/2020/6323965.

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Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.

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Yue, D., J. Gao, and J. B. Chen. "Design and realization of control system for automatic setting machine of artificial flower based on PLC." IOP Conference Series: Materials Science and Engineering 382 (July 2018): 032035. http://dx.doi.org/10.1088/1757-899x/382/3/032035.

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Hoang, Nhat-Duc, Dieu Tien Bui, and Kuo-Wei Liao. "Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine." Applied Soft Computing 45 (August 2016): 173–86. http://dx.doi.org/10.1016/j.asoc.2016.04.031.

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45

Chauhan, Ramesh, Sanatsujat Singh, Vikas Kumar, Ashok Kumar, Amit Kumari, Shalika Rathore, Rakesh Kumar, and Satbeer Singh. "A Comprehensive Review on Biology, Genetic Improvement, Agro and Process Technology of German Chamomile (Matricaria chamomilla L.)." Plants 11, no. 1 (December 23, 2021): 29. http://dx.doi.org/10.3390/plants11010029.

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German chamomile (M. chamomilla) is recognized as a star herb due to its medicinal and aromatic properties. This plant is found across a wide range of climatic and soil conditions. Both the flower heads and blue essential oils of German chamomile possess several pharmacological properties of an anti-inflammatory, antimicrobial, antiseptic, antispasmodic and sedative, etc., nature, which makes it a highly sought after herb for use in many pharma and aroma industries. Chamomile tea, prepared from its flower heads, is also a well-known herbal tea for mind and body relaxation. Though it is a high-demand herb, farmers have not adopted this plant for large scale cultivation as a crop, which could improve their livelihood, due to the high cost in flower heads harvesting, loss in over mature and immature flower heads picking during harvesting, unavailability of varieties and agrotechnologies for machine harvesting, a lack of efficient process development of oil extraction and in the lack of improved stable varieties. There are many studies that have reported on the phytochemistry and pharmacological uses of chamomile, which further explore its importance in the medicine industry. Several studies are also present in the literature on its cultivation practices and plant ecology. However, studies on breeding behavior, genetic improvement, varietal development and mechanical harvesting are scarce in German chamomile. Hence, keeping in mind various aspects of farmers’ and researchers’ interest, earlier reports on taxonomy, floral biology, processing of oil extraction, active constituents, uses, agronomy, breeding challenges and opportunities in German chamomile are summarized in this review.

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46

Cheng, Yang, and Wang Qi. "Study on Mechanical Law of Vibration Abscission of Camellia Oleifera Fruit Based on High-Speed Camera Technology." JOURNAL OF ADVANCES IN AGRICULTURE 10 (April 30, 2019): 1713–25. http://dx.doi.org/10.24297/jaa.v10i0.8265.

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The movement and mechanical characteristics of oil-tea camellia fruit coupling with flower simultaneously during the harvest period were studied to provide an indispensable theoretical guideline for the mechanized picking machine. The mechanical properties of Camellia oleifera were obtained by investigating its flowers and fruits, and the vibration harvesting of Camellia oleifera was studied by using dynamic vibration device. The i-Speed3 high-speed camera was used to record the harvesting process, and the image analysis and calculation were carried out by its own Control-Pro software. The results showed that different varieties of Camellia oleifera had different flowering periods, ranging from 30 to 55 days; their weight also varied among varieties; there was no inevitable relationship between the binding force (pulling force and torque) of fruit stalks and their varieties, locations and diameter of fruit stalks. More importantly, during the vibration process, the fruit peeling speed is related to the vibration source clamping position and vibration parameters. Under the condition of short clamping distance and high frequency and low amplitude, the fruit is more likely to fall off.

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47

Song, Jin Hu. "The Development of Warm Room Ills's Ozone Control Machine Based on Single Chip Computer." Applied Mechanics and Materials 401-403 (September 2013): 2016–20. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2016.

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Realize ozonation of the air by high voltage discharge using air in the warm room as raw materials. Ozone can quickly kill the ills in the air, on the face of the plants and Passivat virus. Therefore, one kind of warm room ills's ozone control machine of simply structure with easy operation and reliable performance has been developed. warm room ills's ozone control machine is a sterilization and disinfection equipment of no pollution and residue, which development and production to prevent and control warm room vegetable aeroborne disease, Is the best technology security equipment for the production of pollution-free green vegetables instead of pesticides. This product can also be used in sterilization fields of flower,edible fungus, storage and all kinds of water treatment and other fields.

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Abdureyim, Ilham, Arkin Hamdulla, Mamatjan Tursun, Kalbinur Ahmat, and Mamtimin Gheni. "FEM Analysis and Optimization Design of a Plastic Film Cutting Machine Tool Rest Structure." Advanced Materials Research 33-37 (March 2008): 1393–98. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1393.

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The numerical controlled plastic film cutting machine in our design is used for flower bags making. It is composed of four parts; cutting mechanism, delivering mechanism, tool rest and machine bed. The tool rest is a critical structure to cutting and adhering operations. The tool rest is a long -thin rod with big span, usually it’s failure is because of bending deformation caused by less stiffness. In this study, we finished FEM 3D modeling of tool rest structure to increase its structure stiffness. We analyzed bending deformation characteristics of the tool rest, performed 3D FEM analysis and choose a relatively reliable structure after comparing maximum deformation in three different candidate structures. As a result, support structure of the tools rest was optimized, providing us a numerical data for structure reformation.

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Ribeiro, Alison Pereira, Nádia Felix Felipe da Silva, Fernanda Neiva Mesquita, Priscila de Cássia Souza Araújo, Thierson Couto Rosa, and José Neiva Mesquita-Neto. "Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds." PLOS Computational Biology 17, no. 9 (September 16, 2021): e1009426. http://dx.doi.org/10.1371/journal.pcbi.1009426.

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Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees–those that are efficient pollinators–is essential to improve the economic returns for farmers. To achieve this, it is important to know the identity of the visiting bees. Nevertheless, the traditional taxonomic identification of bees is not an easy task, requiring the participation of experts and the use of specialized equipment. Due to these limitations, the development and implementation of new technologies for the automatic recognition of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel Frequency Cepstral Coefficients (MFCC) and with classifications based solely on the from fundamental frequency, leading to a direct comparison between the two approaches. In fact, some classifiers powered by the MFCC–especially the SVM–achieved better performance compared to the randomized and sound frequency-based trials. Moreover, the buzzing sounds produced during sonication were more relevant for the taxonomic recognition of bee species than analysis based on flight sounds alone. On the other hand, the ML classifiers performed better in recognizing bees genera based on flight sounds. Despite that, the maximum accuracy obtained here (73.39% by SVM) is still low compared to ML standards. Further studies analyzing larger recording samples, and applying unsupervised learning systems may yield better classification performance. Therefore, ML techniques could be used to automate the taxonomic recognition of flower-visiting bees of the cultivated tomato and other buzz-pollinated crops. This would be an interesting option for farmers and other professionals who have no experience in bee taxonomy but are interested in improving crop yields by increasing pollination.

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Widyastuti Santiary, Putri Alit, I. Ketut Swardika, Ida Bagus Irawan Purnama, I. Wayan Raka Ardana, I. Nyoman Kusuma Wardana, and Dewa Ayu Indah Cahya Dewi. "Labeling of an intra-class variation object in deep learning classification." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 179. http://dx.doi.org/10.11591/ijai.v11.i1.pp179-188.

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<span lang="EN-US">Machine orientation learning had demonstrated that deep learning (DL)-convolutional neural networks (CNNs) were robust image classifiers with significant accuracy. Although to been functional, DL scope classification as tight, well-defined as possible uses a 2-class object, for instance, cats and dogs. The DL classification faced many challenges, e.g., variation factors, the intra-class variation. This nature is presented in every object, its diversity of an object. The label was an exact given name of an intra-class variation object. Unfortunately, not every object had a specific name, in exceptionally high similarity inside the category. This paper explored those problems in flower plants’ taxonomy naming. In supervised learned of DL, image datasets musted labeled with a meaningful word or phrase that humans are familiar with, a taxonomy naming. Labeled with visual feature extraction brought a fully automatic classification. Flower Plumeria L labeling extracted from perspective dimension scale of petal flower which automatically obtained by contour detection, and peaks of blue green red (BGR) histogram channels from bins histogram after object masked. Dataset collected on photography workbench equipped with webcam and ring light. Results showed labels for intra-class variation of Plumeria L in form of dimension-scale and BGR-peaks. The result of this study presented a novelty in building datasets for intra-class variation for the DL classification.</span>

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