scholarly journals Classification of hand‐wrist maturity level based on similarity matching

2021 ◽  
Author(s):  
Keji Mao ◽  
Lijian Chen ◽  
Minhao Wang ◽  
Ruiji Xu ◽  
Xiaomin Zhao
Keyword(s):  
Maturity Level ◽  
Similarity Matching

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

scholarly journals Viewpoint Analysis for Maturity Classification of Sweet Peppers

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3783
Author(s):  
Ben Harel ◽  
Rick van Essen ◽  
Yisrael Parmet ◽  
Yael Edan
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Sweet Pepper ◽  
Classification Performance ◽  
Robotic Arm ◽  
Random Forest Algorithm ◽  
Maturity Level ◽  
Depth Images ◽  
Manual Classification

The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

scholarly journals TOWARDS PROCESS MATURITY – TRIGGERS OF CHANGE

2016 ◽  
Author(s):  
Arkadiusz Jurczuk
Keyword(s):  
Literature Review ◽  
Success Factors ◽  
Maturity Level ◽  
Process Maturity ◽  
Maturity Models ◽  
Contextual Model ◽  
Process Oriented

Process-oriented development of an organization is supported by maturity models. They delineate a sequence of stages that together form a path from an initial to a desired maturity level. Maturity progress is determined by various success factors and initiated by different triggers. There is no common classification of these triggers. The purpose of this paper is to identify triggers in relation to maturity levels. To identify triggers a contextual model has been developed and literature review has been conducted. Results suggest that due to incomplete understanding of process-based approach some additional triggers may also affect success of its implementation.

scholarly journals Analysis of business processes of agricultural enterprises for the implementation of IT innovations

2020 ◽  
Author(s):  
T.S. Lisienkova ◽  
◽  
E.A. Isaev ◽  
Keyword(s):  
Information Technologies ◽  
Business Processes ◽  
Digital Transformation ◽  
Animal Breeding ◽  
Maturity Level ◽  
Agricultural Enterprises ◽  
Core Business ◽  
Breeding Farms ◽  
It Innovations

This article touches upon digital transformation in agriculture. Firstly, classification of core business processes of agriculture is presented with examples of primary and support business processes at animal breeding farms. Later the article gives an overview of innovative information technologies used nowadays in agriculture worldwide. Finally, it is justified that for the successful digital transformation it is vital to evaluate current IT-maturity level of business processes in order to fix their “bottlenecks” before the implementation of innovative information technologies. While common models of IT-maturity, level does not take into account features of specific industries, it is necessary to develop a stand-alone model for agricultural companies.

scholarly journals K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors

2019 ◽  
Vol 2 (2) ◽  
pp. 101
Author(s):  
Suwanto Sanjaya ◽  
Morina Lisa Pura ◽  
Siska Kurnia Gusti ◽  
Febi Yanto ◽  
Fadhilah Syafria
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Image Size ◽  
K Nearest Neighbor ◽  
Color Information ◽  
Maturity Level ◽  
Total Data ◽  
Fold Cross Validation ◽  
Selection Of

The selection of tomatoes can use several indicators. One of the indicators is the fruit color. In digital image processing, one of the color information that could be used in Hue, Saturation, and Value (HSV). In this research, HSV is proposed as a color model feature for information on the ripeness of tomatoes. The total data of tomato images used in this research were 400 images from four sides. The maturity level of tomatoes uses five levels, namely green, turning, pink, light red, and red. The process of divide data uses K-Fold Cross Validation with ten folds. The method used for classification is k-Nearest Neighbor (kNN). The scenario of the test performed is to combine the image size with the parameter value of the neighbor (k). The image sizes tested are 100x100 pixels, 300x300 pixels, 600x600 pixels and 1000x1000 pixels. The “k” values tested were 1, 3, 5, 7, 9, 11, and 13. The highest accuracy reached 92.5% in the image size 1000x1000 pixels with a parameter “k” is 3. The result of the experiment showed that the image size has a significant influence of accuracy, but the parameter value of neighbor (k) has an influence that is not too significant.

scholarly journals Effect of Fruit Maturity and Extraction Treatment on Germination Percentage of Langusei (Ficus minahassae (Teysm.et.Vr.) Miq))

Jurnal Wasian ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 103-109
Author(s):  
Arif Irawan ◽  
Keyword(s):  
Germination Percentage ◽  
Maturity Level ◽  
Color Categories ◽  
Fruit Maturity ◽  
Drying Treatment ◽  
Endemic Flora ◽  
Cultivation Techniques ◽  
Germination Techniques ◽  
Extraction Treatment

Langusei (Ficus minahassae (Teysm.et.Vr.) Miq) is one of the endemic flora of Sulawesi which existence is increasingly threatened. Attempts to maintain the existence of Ficus minahassae species by understanding the cultivation techniques of the Langusei species, especially regarding the appropriate germination techniques, need to be addressed. This study aims to determine the level of fruit maturity and the appropriate extraction treatment for the germination of Langusei seeds. The experimental design used in this study was a complete randomization design which is arranged in a factorial pattern. The first factor is the classification of the level of seed maturity based on fruit color categories: 1) Orange-brown fruit, 2) Orange-reddish fruit, 3) Red fruit, and 4) Red-black fruit, while the second factor is the fruit extraction treatment which consists of: 1) Extraction with dry wind treatment for 24 hours, 2) Extraction with drying treatment for 12 hours, and 3) Extraction with water treatment for 24 hours. The results showed that the fruit maturity level and the appropriate extraction treatment to produce optimal Langusei seed germination were in the (orange-brown and orange-reddish fruit) (mature fruits prior to ripening) which was soaked in water for 24 hours.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

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