scholarly journals Tomato maturity recognition using YOLOV 5 machine learning

2021 ◽  
Vol 82 (4) ◽  
pp. 49-60
Author(s):  
Zh. A. Buribayev ◽  
◽  
Zh. Amirgaliyeva ◽  
S. K. Joldasbayev ◽  
M. S. Zhassuzak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Production Management ◽  
Agricultural Sector ◽  
Confusion Matrix ◽  
Life Cycles ◽  
Classification Problems ◽  
Tomato Fruits ◽  
Automatic Feature Extraction ◽  
Extraction Behavior ◽  
The Republic

The implementation of robotic systems and digitalization in agriculture are important tasks today. In this paper, the possibility of using pattern recognition and machine learning methods as a computer model of an agricultural robot for harvesting is considered. The grading of tomato fruits can be classified based on their ripeness according to their life cycles, which can be identified by their color: green in the growing stage, yellow in the pre-ripening stage, and red when ripe. Conventional skill-based methods cannot meet the exact selection criteria for modern production management in the agricultural sector as they are time-consuming and of low accuracy. Automatic feature extraction behavior using machine learning is most effective in image classification and recognition tasks. Thus, the article presents the results of a study on the recognition of ripe tomato fruits by a robotic system, carried out within the framework of the grant project of the Ministry of Education and Science of the Republic of Kazakhstan AP08857573 and implemented classical algorithms based on the HSV color model and color segmentation using the k-means algorithm as comparative algorithms and based on machine learning, a universal intelligent tomato classification system is proposed for practical use using Yolo 5. This study aims to provide an inexpensive solution with the best performance and accuracy for assessing tomato ripeness. The results are collected in terms of accuracy, loss curves and confusion matrix. The results showed that the proposed model outperforms other machine learning (ML) methods used by researchers for tomato classification problems, providing 99% accuracy.

scholarly journals An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

2021 ◽  
Vol 26 (2) ◽  
pp. 191-200
Author(s):  
Prasenjit Das ◽  
Jay Kant Pratap Singh Yadav ◽  
Arun Kumar Yadav
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Low Cost ◽  
Confusion Matrix ◽  
Grading System ◽  
Learning Networks ◽  
Automatic Feature Extraction ◽  
Agriculture Sector ◽  
Extraction Behavior ◽  
Automated Grading

Tomato maturity classification is the process that classifies the tomatoes based on their maturity by its life cycle. It is green in color when it starts to grow; at its pre-ripening stage, it is Yellow, and when it is ripened, its color is Red. Thus, a tomato maturity classification task can be performed based on the color of tomatoes. Conventional skill-based methods cannot fulfill modern manufacturing management's precise selection criteria in the agriculture sector since they are time-consuming and have poor accuracy. The automatic feature extraction behavior of deep learning networks is most efficient in image classification and recognition tasks. Hence, this paper outlines an automated grading system for tomato maturity classification in terms of colors (Red, Green, Yellow) using the pre-trained network, namely 'AlexNet,' based on Transfer Learning. This study aims to formulate a low-cost solution with the best performance and accuracy for Tomato Maturity Grading. The results are gathered in terms of Accuracy, Loss curves, and confusion matrix. The results showed that the proposed model outperforms the other deep learning and the machine learning (ML) techniques used by researchers for tomato classification tasks in the last few years, obtaining 100% accuracy.

DEVELOPMENT PROSPECTS OF KAZAKHSTAN AGRARIAN SECTOR

2020 ◽  
Vol 10 (7) ◽  
pp. 1257-1265
Author(s):  
S.M. Kantarbaeva ◽  
Keyword(s):  
Agricultural Production ◽  
Production Management ◽  
Production Efficiency ◽  
Agricultural Sector ◽  
Economic Effect ◽  
Industrial Sector ◽  
Agricultural Products ◽  
Industrial Complex ◽  
The Republic

The problems of agricultural production management, the prospects for its development in a crisis (the resource one and pandemic), creation of necessary conditions for the effective activity of commodity producers are considered. The results of the main trade relations for the Republic of Kazakhstan in 2015-2019 and the priorities of the state internal agricultural policy are analyzed within the framework of the geostrategic goals of developing the market for agricultural products, raw materials and food, as well as creating favorable conditions for the activity of an agricultural producer. Basing on the analysis, the main reasons for the existing production problems in the agro-industrial complex of the republic were revealed: production technical and technological ones, underdeveloped production infrastructure, segmental approach to financing agricultural producers and their associations in the absence of comprehensive measures for the development of the agro-industrial sector, low prestige of labor in agriculture, insufficient investment attractiveness and the significant influence of external factors on the production efficiency. The article provides statistical data showing trends in foreign trade in the context of the main types of agricultural products and partner countries. The role of the Chinese agri-food market on the development of Kazakh agricultural production and the consumer market is shown. The role of the One Belt and One Road initiative in the formation of commodity flows, including transit of goods and the economic effect of developing other fields. The features of agricultural production in the crisis are highlighted, which allow the use of digital tools and other economic measures to activate processes in the agricultural sector. The need for active application of republican sustainable development goals that contribute to solving the problems of hunger, poverty and caring for future generations is indicated.

scholarly journals Comparative Study on Reliability of Transfer Learning to Classify Plant-Based Diseases

2021 ◽  
Vol 10 (6) ◽  
pp. 154-160
Author(s):  
N Rohan Sai ◽  
◽  
T Sudarshan Rao ◽  
G. L. Aruna Kumari ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Architecture ◽  
Agricultural Sector ◽  
Performance Metrics ◽  
Primary Objective ◽  
The Other ◽  
Common Disease ◽  
Classification Problems

One of the essential factors contributing to a plant's growth is identifying and preventing diseases in the early stages. Healthy plants are essential for a rich production. Recent advances in Deep learning - a subset of Artificial Intelligence and Machine Learning are playing a pivotal role in solving image classification problems and can be applied to the agricultural sector for crop surveillance and early anomaly identification. For this research, we used an open-source dataset of leaf images divided into three classes, two of which are the most common disease types found on many crops; the graphical characterizations for the three classes are images of leaves with Powdery Residue, images of leaves with Rusty Spots, and images of Healthy leaves. The primary objective of this research is to present a pre-trained ImageNet network architecture that is well suited for dealing with plant-based data, even when sample sizes collected are limited. We used different convolutional neural network-based architectures such as InceptionV3, MobileNetV2, Xception, VGG16, and VGG19 to classify plant leaf images with visually different representations of each disease. Xception, MobileNetV2, and DenseNet had a considerable advantage over all the performance metrics recorded among the other networks trained.

scholarly journals ACTUAL ISSUES OF FINANCIAL STABILITY OF THE AGRICULTURAL SECTOR OF UZBEKISTAN

2019 ◽  
Vol 1 (8(38)) ◽  
pp. 32-37
Author(s):  
Bafoyev Otabek
Keyword(s):  
Financial Stability ◽  
Production Management ◽  
Agricultural Sector ◽  
Industrial Sector ◽  
Sustainable Growth ◽  
Industrial Complex ◽  
Current State ◽  
Global Trend ◽  
Production Management System ◽  
The Republic

This article examines the prospects for increasing the competitiveness of the agro-industrial complex of the Republic of Uzbekistan through the formation and development of clusters as an innovative type of production management, outlines the main problems and the current state of the domestic agro-industrial sector, which forms national food security, directions and mechanism for the development of factors aimed at creating conditions for sustainable growth of the country's economy. At present, characterized by the global trend of growing global competition, the problem of finding new factors that can ensure the growth of national competitiveness and sustainable development of the state’s economy arises, and this makes this article relevant.One of the means of updating the production management system to solve this problem is the cluster approach, which can be effectively implemented, when will be taken into account the specific conditions of certain areas of economic activity.

scholarly journals Опыт трудовой миграции сельских жителей Калмыкии (по результатам глубинных интервью)

2020 ◽  
Vol 13 (3) ◽  
pp. 688-695
Author(s):  
Nogan V. Badmaeva ◽  
Keyword(s):  
Migrant Workers ◽  
Labor Migration ◽  
Agricultural Sector ◽  
Life Cycles ◽  
Living Standards ◽  
Migrant Labor ◽  
Structured Interviews ◽  
Labor Migrants ◽  
Family And Marriage ◽  
The Republic

Introduction. Labor migration of Kalmykia’s rural population is a pressing challenge for the region. Permanent nature and endurance of the socioeconomic crisis in the agricultural sector of the republic have been adversely affecting the living standards of ordinary villagers. Lack of work opportunities and low salaries result in that the latter migrate en masse to the regional capital and even further. Goals. The study aims to analyze labor migration experiences of local rural dwellers. Materials and Methods. The paper summarizes a number of in-depth structured interviews. The qualitative research methods employed make it possible to view the issue in the eyes of unrelated actual participants of the migration processes, with certain attention paid to their backgrounds and life paths. Results. The work reveals one of the key economic factors underlying labor migration is the necessity to pay mortgage and consumer loans. And migration waves closely align with individual life cycles, such as marriage, divorce, births and even weddings of children. Some respondents reported their migrations were determined by certain adulthood stages of children. All these aspects give rise a new context of family and marriage relations: there emerge guest marriage patterns and changes in gender roles, e.g., in some families those are women who act as migrant workers. Roles of grandparents experience transformations forcing the latter to assume functions of the absent father of mother. Horizontal social networks come to the fore, including territorial and kindred ties. Such migrant labor experiences become a tool of economic strategies and mobility: people purchase dwellings in the city, and support children funding their plans with the earned money. So, migration of parents definitely serves a landmark for future migrations of their descendants. The results obtained attest to that the social profile of rural labor migrants contains quite a share of active individuals intensely motivated to work, ones who strive for better living standards and can adjust themselves to strenuous living / working conditions staying away from home and family.

scholarly journals Development of Machine Learning Strategy for Predicting the Risk Range of Ship’s Berthing Velocity

2020 ◽  
Vol 8 (5) ◽  
pp. 376
Author(s):  
Hyeong-Tak Lee ◽  
Jeong-Seok Lee ◽  
Woo-Ju Son ◽  
Ik-Soon Cho
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Gradient Boosting ◽  
Classification Algorithms ◽  
Factors Affecting ◽  
Machine Learning Classification ◽  
The Republic

Ships are prone to accidents when approaching in a berthing velocity greater than that allowed when determining the risk range corresponding to a port. Therefore, this study develops a machine learning strategy to predict the risk range of an unsafe berthing velocity when the ship approaches in port. To perform analysis, the input parameters were based on the factors affecting the berthing velocity, and the output parameter, i.e., the berthing velocity, was measured at a tanker terminal in the Republic of Korea. Nine machine learning classification algorithms were used to analyze each model, and the top four optimal models were selected through evaluation methods based on the confusion matrix. As a result of the analysis, extra trees, random forest, bagging, and gradient boosting classifiers were identified as good models. As a result of testing using the receiving operator characteristic curve, it was confirmed that the area under the curve of the most dangerous range of berthing velocity was the highest, thus, the risk range was appropriately classified. As such, the derived models can classify and predict the risk range of unsafe berthing velocity before approaching a port; therefore, it is possible to safely berth a ship.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Incentives of the State to promote the retrofitting of the agricultural sector: Regional considerations

2020 ◽  
Vol 16 (6) ◽  
pp. 1101-1113
Author(s):  
M.Sh. Gutuev ◽  
B.Sh. Ibragimova
Keyword(s):  
Agricultural Sector ◽  
Animal Husbandry ◽  
Technological Equipment ◽  
Agricultural Producers ◽  
Agricultural Technologies ◽  
Abstract Logic ◽  
Land Cultivation ◽  
Equipment Acquisition ◽  
Acquisition Costs ◽  
The Republic

Subject. The article discusses the availability of technological equipment in the agriculture of the Republic of Dagestan. Objectives. We analyze the current situation and trends in the development of available technological equipment in the republican agriculture, identify the role and place of machine and tractor fleet in the retrofitting of the regional agriculture. Methods. The study is based on monographic, abstract logic, statistical, analytical methods. Results. Dagestan has got a critically few technological equipment, which affects the development of the regional agricultural sector. The availability of technological equipment is found to have dramatically reduced in animal husbandry for the recent 30 years. The availability of technological equipment and land cultivation strongly correlates, thus simplifying applicable agricultural technologies. Most agricultural producers of Dagestan were found to be unable to participate in the program for federal agricultural lease. Conclusions and Relevance. The deterioration of available technological equipment in agriculture is a key cause undermining the competitiveness of products and efficiency of the regional agriculture. We prove the importance of governmental actions incentivizing the influx of new technological equipment, including a set of measures reinforcing the availability of technological equipment. As long as most agricultural producers are microbusinesses that lack resources to participate in many machine renovation programs, funding should be increased substantially to subsidize a portion of equipment acquisition costs incurred by agricultural producers, and a portion of reimbursed costs as much as at least 50 percent of the value of agricultural machines acquired.

CURRENT STATE AND PROSPECTS FOR THE DEVELOPMENT OF BIOGAS ENERGY IN UZBEKISTAN

2020 ◽  
Vol 2 (7) ◽  
pp. 171-184
Author(s):  
Z. U. SAIPOV ◽  
◽  
G. A. ARIFDZHANOV ◽  
Keyword(s):  
Renewable Energy ◽  
Organic Waste ◽  
Agricultural Sector ◽  
Renewable Energy Sources ◽  
Energy Resources ◽  
Agricultural Producers ◽  
Biogas Plants ◽  
The Republic ◽  
Rural Producers ◽  
Agricultural Regions

Energy is one of the main pillars of the state’s economy, which is currently facing serious problems due to depletion of mineral energy resources and the threatening environment. As a result, presently around the world there is a rapid growth and development of energy-efficient technologies and the use of renewable energy sources (RES), providing an increase in energy resources, as well as environmental and social effects. One of the most relevant and promising areas of renewable energy development is the disposal and processing of organic waste in biogas plants, and this is particularly relevant in agricultural regions. In this regard, this paper considers the state and prospects for the development of bioenergy in agricultural regions of Uzbekistan, where half of the population of the republic lives. The potential of organic waste from livestock and poultry farming of the agricultural sector was determined, and it was revealed that the use of biogas plants for the disposal of manure and litter is clearly a profitable production and requires close attention from rural producers. The introduction of biogas technologies for the bulk of agricultural producers is an urgent task, that will ensure not only a solution to the waste problem, but it will also provide a solution to energy, agricultural, environmental and social problems in rural regions of the republic.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

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