Kartanova Asel
@kstu.kg
Higher School of Economics and Business
KSTU Kyrgyzstan
RESEARCH INTERESTS
computer simulation fluid dynamics, fluid and gas mechanics, information technologies, computer added engeeniring
Scopus Publications
Scopus Publications
A.Dzh. Kartanova, Zh.R. Sarypbekova, Z.A. Sydykova, and Zh.B. Mamadalieva
EDP Sciences
During the era of digital transformation in Kyrgyzstan, the implementation of innovative solutions based on artificial intelligence technologies and the development of information systems for smart poultry farms can become a key element of sustainable agricultural development and is a pressing issue. This study presents an approach to the conceptual modeling of an intelligent control system for smart poultry farms. This work presents the development of a digital model of the intelligent system of the smart-bird farm, based on which an approach to the integration of Internet of Things (IoT), computer vision and big data processing technologies is implemented. An object-oriented development of an intellectual system using the universal modeling language UML was carried out. The structure of the digital model is presented, the functional components of the system are selected, the objects of the system are considered and the relationship between them, the information flows between the objects and components of the system are described.
Baratbek Sabitov, Nurzat Asanbekova, Nazgul Seitkazieva, Asel Kartanova, and Kyzylgul Jentaeva
SPIE
In this paper, various architectures of neural networks of deep learning technologies were created to model viral diseases of poultry. Models for recognizing coccidiosis, salmonellosis, and Newcastle disease, which are common diseases in poultry farming, were built. The constructed models for detecting poultry diseases are aimed at diagnosing sick birds at an early stage. Convolutional neural networks and deep learning models were created, which are based on predicting poultry diseases by classifying healthy and unhealthy feces images of four types. Unhealthy feces images that can be symptoms of coccidiosis, salmonellosis, and Newcastle disease were identified. A model was built using the base model of convolutional neural networks of various architectures. The models were trained using feces images labeled by the farm and the laboratory, and then their hyperparameters of the neural network were fine-tuned. The test set used images labeled with our own data taken from poultry farms. The test accuracy results without fine-tuning are obtained, which amounted to 85.06% for the base CNN, 87.85% for the updated CNN. Fine-tuning while freezing the batch normalization layer improved the accuracy of the model to 95.01% with F1 scores for all classifiers above 83% in all four classes. Considering the smaller weight of the model trained with the CNN and its better generalization ability, we recommend using this model for early detection of poultry diseases at the farm level. The main result of the work is the implementation of models based on transfer learning of VGG 16, ResNet 18 and Efficient Net models. The efficiency of the transfer approach to building models and their comparative analysis are shown. Error matrices and reports on the classification of poultry diseases based on transfer learning up to 99% accuracy are built.
Baratbek Sabitov, Asel Kartanova, Talant Kurmanbek uulu, Nazgul Seitkazieva, Ainura Dyikanova, and Aida Orozobekova
EDP Sciences
Continuous advances in computer technology have provided good support for the expansion of agricultural research using machine learning. This article considered the current problem of yield forecasting using methods and algorithms of machine learning to support management decision-making in the agricultural sector. For a set of data collected from five districts of the Issyk-Kul region, such as weather conditions, soil characteristics and pre-processing of the sowing area, a study of the yield of various crops using advanced machine learning algorithms, such as the support vector method, k-nearest neighbors, variants of gradient boosting and random forest, etc., is demonstrated. To assess the accuracy of the models, a comparative analysis with the results of multiple regression was carried out. It is shown that powerful regression machine learning algorithms like k-nearest neighbors (KNN), random forest (RF), support vector method (SVR) and gradient boosting (GBR) give tangible results in prediction compared to other machine learning methods (MAPE=10%). The calculation results showed the effectiveness of using algorithms with ensemble methods to solve the problems of yield forecasting, and that environmental factors (weather conditions) have a greater impact on yield than soil genotype.
ZhV Chashina, , AD Kartanova, and
National Research Mordovia State University MRSU