ДАУКЕЕВА»
ISSN 2790-0886
В Е С Т Н И К
АЛМАТИНСКОГО УНИВЕРСИТЕТА ЭНЕРГЕТИКИ И СВЯЗИ
Учрежден в июне 2008 года
Тематическая направленность: энергетика и энергетическое машиностроение, информационные, телекоммуникационные и космические технологии
1 (60) 2023
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о постановке на переучет периодического печатного издания, информационного агентства и сетевого издания
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Министерством информации и общественного развития Республики Казахстан
Подписной индекс – 74108
Бас редакторы – главный редактор Стояк В.В.
к.т.н., профессор
Заместитель главного редактора Жауыт Алгазы, доктор PhD Ответственный секретарь Шуебаева Д.А., магистр
Редакция алқасы – Редакционная коллегия
Главный редактор Стояк В.В., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Заместитель главного редактора Жауыт А., доктор PhD, ассоциированный профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Сагинтаева С.С., доктор экономических наук, кандидат физико-математических наук, профессор математики, академик МАИН;
Ревалде Г., доктор PhD, член-корреспондент Академии наук, директор Национального Совета науки, Рига, Латвия;
Илиев И.К., доктор технических наук, Русенский университет, Болгария;
Белоев К., доктор технических наук, профессор Русенского университета, Болгария;
Обозов А.Д., доктор технических наук, НАН Кыргызской Республики, заведующий Лабораторией «Возобновляемые источники энергии», Кыргызская Республика;
Кузнецов А.А., доктор технических наук, профессор Омского государственного технического университета, ОмГУПС, Российская Федерация, г. Омск;
Алипбаев К.А., PhD, доцент Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Зверева Э.Р., доктор технических наук, профессор Казанского государственного энергетического университета, Российская Федерация, г. Казань;
Лахно В.А., доктор технических наук, профессор Национального университета биоресурсов и природопользования Украины, кафедра компьютерных систем, сетей и кибербезопасности, Украина, Киев;
Омаров Ч.Т., кандидат физико-математических наук, директор Астрофизического института имени В.Г. Фесенкова, Казахстан;
Коньшин С.В., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан;
Тынымбаев С.Т., кандидат технических наук, профессор Алматинского Университета Энергетики и Связи имени Гумарбека Даукеева, Казахстан.
За достоверность материалов ответственность несут авторы.
При использовании материалов журнала ссылка на «Вестник АУЭС» обязательна.
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ИНФОРМАЦИОННЫЕ,
ТЕЛЕКОММУНИКАЦИОННЫЕ И КОСМИЧЕСКИЕ ТЕХНОЛОГИИ
МРНТИ 28.23.37 https://doi.org/10.51775/2790-0886_2023_60_1_111
DEVELOPMENT OF A NEURAL NETWORK MODEL FOR PREDICTION OF THE EFFECT OF PHOSPHORUS ON THE YIELD OF SPRING WHEAT
S.Ye. Sharipova*, А.S. Akanova
S.Seifullin Kazakh Agrotechnical University, Astana, Kazakhstan
e-mail: [email protected], [email protected]
Abstract. The article is devoted to the issue of forecasting the yield of spring wheat. The purpose of this study is to develop a neural network model for predicting the effect of phosphorus on the yield of spring wheat. The author touches upon such issues as the use of information technology, artificial intelligence at the enterprises of the agro- industrial complex of the Republic of Kazakhstan.
Within the framework of this article, a review of existing research in this area was carried out. And the problems of the lack of a neural network for predicting the effect of phosphorus on the yield of spring wheat are consecrated. In this regard, data on phosphorus and nitrogen application, spring wheat yield for the last 24 years were collected, which were later used to train a neural network, which aims to predict the influence of phosphorus on the yield of spring wheat.
As part of this study, the influence of such parameters as the application of fertilizers containing phosphorus and nitrogen, average air temperature, humidity, precipitation on wheat yields from 1997 to 2021 is considered. The input data was pre-processed using a robust method, which were then converted into vectors. Subsequently, a neural network model was developed, consisting of 4 layers: Embedding, Dense32, Dense33 and Out. To create a neural network, the Python library - Keras was used. The article can be used by researchers in the field of forecasting in agriculture, agronomists and IT specialists.
Keywords: neural networks, forecasting, yield, data normalization, spring wheat.
Introduction.
The protein of agricultural crops serves as the primary criterion of quality. A variety of minerals, especially nitrogen, are involved in the production of protein compounds. Anyway with sufficient nitrogen diet, a severe phosphorus deficiency negatively impacts the formation of nucleic acids hence, via them, the protein composition [1]. Wheat consumes more nitrogen as a result of phosphorus, and this rise in nitrogen uptake benefits crop yield and quality. When crops receive enough nitrogen from phosphate fertilizers, grain plants' crude protein can rise. Because phytate has the ability to prevent the production of aflatoxin, grains with greater phosphorus content can result in healthier diets [2]. The energy of fertilization, the construction of the plant root, and, ultimately, the production of a bigger crop yield can all be impacted by an increment phosphorus in the grain [3, 4].
Spring wheat is the most important feed crop among grains. It is one of the most demanding crops in terms of growing conditions. Determination of the influence of the phosphorus on the yield of spring wheat will help to organize sowing most effectively.
The agro-industrial complex uses such methods for forecasting agricultural processes as methods of simple extrapolation, exponential smoothing, moving averages, harmonic weights, and autoregression models [5].
Among the most successful options for studying social, biological, economic, financial, as well as some other complex things is the utilization of neural networks.
The majority of scientists throughout the world [6–10] feel that due to their inconsistency and efficiency, standard methods, techniques, and systems are unable to handle issues in various fields. This
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issue can be resolved with the aid of a neural network, which enables the farmer to uncover hidden connections and draw attention to the most important elements using a large amount of data.
At present, the difficulty of prediction lies in the fact that an agronomist subjectively assesses the influence of various factors on productivity, based only on his knowledge and experience, while neural networks can process a large amount of information in a short time. Forecasting by standard methods takes a lot of effort and time, when with the help of neural networks it is possible to perform a prediction in much less time. Therefore, research on the development of neural networks for predicting yields is now relevant.
The purpose of this study is to develop a neural network model for predicting the effect of phosphorus on the yield of spring wheat. To achieve this goal, the following tasks will be solved:
- input data preparation;
- development of a neural network model for predicting the effect of phosphorus on the yield of spring wheat.
Nowadays, scientists in their research make extensive use of neural networks in forecasting financial markets, oil prices and data analysis. The use of a recurrent neural network for data analysis and stock market forecasting leads to the conclusion that the quality of the network depends on the processing of input data and their subsequent structuring. A comparative analysis of various oil price forecasting methods shows that neural networks provide the opportunity to make the most accurate forecasts [11-12].
Machine learning, deep learning, GEE, and multiple Deep learning modeling techniques, such as Multi - layer perceptron and Convolutional neural networks, with rrBLUP have all been used to predict attributes of wheat. These comparisons have revealed approaches that result in more accurate forecasting. Using LSTM to build a yield prediction model using meteorological and soil data has had good results. The difference between them is that whereas one of them included soil data from a complete connected layer of its neural model, the other pretty much kept the soil data constant across all time scales and put it straight into thr layer long short term memory. [13-14].
Using satellite technology, soil and meteorological data, a DL model predicting winter crop yield in major crop fields of China at the county level was trained using detrended yield statistics and estimated through one-year validation and showed satisfactory accuracy of forecasting the yield of winter wheat. In the same way, the ensemble method was considered, combining the random forest algorithm and the deep neural network algorithm. The results have showen that the proposed approach gives the best yield prediction accuracy [15-16].
Various methods and algorithms of neural networks behave differently depending on the input data.
So, there was a comparison of several sets of input data and several machine learning algorithms for predicting wheat yields in two provinces of Algeria. The LASSO method and the backpropagation method have been considered in yield prediction. According to the results of this study, the training accuracy was higher for the LASSO method [17-18]. In the same way, within the framework of the study [19], a model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA methods, operator of the least absolute reduction and selection (LASSO) and elastic network (ENET). When studying these models, LASSO and the elastic network showed excellent results.
Applying eight supervised machine learning models and evaluating their predictive performance to compare yield prediction results suggests that non-linear models such as CNN, deep neural network, and XGBoost better understand the relationship between yield and input data compared to linear models. [20].
All of the above suggests that the utilizing of NN is a quality-forecasting tool. It leads to the conclusion that there are quite a lot of neural network models for forecasting crop yields based on such parameters as: climatic indicators, tillage depth, nutritional background, etc. The limitation of this research is the absence of a model of NN that can forecast the effect of phosphorus on the yield of spring wheat. All this necessitates a detailed study of the development of a neural network model for predicting the effect of phosphorus on the yield of spring wheat.
Materials and methods of research.
The study examines the influence of the application of fertilizers containing phosphorus and nitrogen, average air temperature, humidity, precipitation on wheat yield from 1997 to 2021. When input values are very different, and sometimes even of different types, this can negatively affect neural network learning. In this regard, before training the neural network, its input data must be normalized. Preliminarily, the data were normalized, which are necessary for training the neural network to predict the effect of phosphorus on the yield of spring wheat [21].
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In a data normalization study, there are various ways and methods that have a significant impact on the result of normalization.
Min-max normalization. This method is widely used in data normalization. According to the rules of this method, the minimum and maximum elements of the list are first found, then they are equal to 0 and 1, respectively, and the remaining elements will be between 0 and 1. The formula of this method is shown in Table 1.
Minimax normalization does not take into account anomalous values, which can adversely affect data analysis and subsequent training of the neural network as a whole. In Z-normalization, this case is already taken into account by using the average value. The formula of this method is also indicated in Table 1, as well as the formula of the third normalization method – robust (interquartile normalization).
Robust normalization considers the interquartile ranges of the value in which the "central" 50% of the data set is located. This value does not depend on the “normality” of the distribution of the presence/absence of asymmetry and is already resistant to outliers.
Table 1 – Normalization methods
Method Formula
Min-max
𝐴𝑖 = 𝐴𝑖− 𝑚𝑖𝑛𝐴𝑖 𝑚𝑎𝑥𝐴𝑖− 𝑚𝑖𝑛𝐴𝑖
Z-average 𝐴𝑖=𝐴𝑖− 𝜇
𝜎
Robust 𝐴𝑖 = 𝐴𝑖− 𝑚𝑒𝑑𝑖𝑎𝑛𝑎
75 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 − 25 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝐴𝑖− 𝑖-element
𝜇 − minumal value of function 𝜎 − function standard deviation
In this study, the input data are the volume of fertilizer with phosphorus, nitrogen, average air temperature, humidity and precipitation.
The normalized input data can be seen in Table 2 for the 2002-2004 calculation results for phosphorus and nitrogen for clarity.
Table 2 – Input normalized data
Year Phosp Nitr Yield MMPh ММN ZPh ZN RPh RN 2002 198,5 803,4 10,8 0,33435 0,20882
8
0,008964 0,136985 1,60411 9
0,68561 4
2003 158,4 759,8 11,6 0,19850 9
0,17748 5
0,000477 0,127758 0,68649 9
0,46629 8
2004 169,1 702,4 8,4 0,23475 6
0,13622 3
0,002742 0,115609 0,93135 0,17756 5
From table 2 these are:
- ММPh – Minimax Normalization for Phosphorus - ММN – Minimax normalization for nitrogen - ZPh – Z- average for phosphorus
- ZN– Z- average for nitrogen - RPh – robust for phosphorus - RN – robust for nitrogen
Robust normalization results were used for the developed neural network. When different types of normalization methods are applied to the same dataset using the same machine learning method, the result can be different. Robust normalization is easy to use and gives better results than other normalization methods.
The next step is to develop a neural network model to predict the effect of phosphorus on the yield of spring wheat.
Time-series prediction is still an important task nowadays, primarily in light of the development of efficient data collection strategies and analysis. Since it enables the prediction of the behavior of different
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components in environmental, financial, sociological, as well as other sectors, time-series prediction is a significant scientific and technological subject. [22].
Research methods such as analysis and classification were used to solve the problem. The types of neural networks on the classification criteria were considered and the best solution was chosen.
Figure 1 shows a multilayer neural network with its hidden layers and input parameters.
Рисунок 1 – Многослойная нейронная сеть
Figure 1 – Multilayer neural network
It is required to determine the parameters which will be forecasted, and the prediction accuracy in order to resolve the forecasting problem. When resolving forecasting issues, it is frequently required to foresee changes in the variable's values rather than the measure itself. The forecasting system is significantly impacted by the level of forecasting accuracy necessary for a given situation. The forecasting system utilized affects the prediction error. A system that has more capabilities has a greater possibility of producing a prediction that is more precise.
Since NN models may learn and can uncover hidden links and connections between dataset, and training techniques modify weights based on the format of the data provided for training, using neural networks requires limited analyst participation in the creation of a model of time series.
The process of using NN to predict data series entails the construction of a specific structure, the parametric conditions of the network based on the actions of the system at predefined periods of time, and the prediction of the system's future actions based on its past behavior. The specificity and difficulty of the issue that needs to be solved dictate the neural network's selection of architecture.
The Sequential model creates a skeleton to create a model of the neural network. Four layers were selected as the main layers: Embedding, Dense 32, Dense 33 and Out, where Embedding is the input layer, Dense 32 and Dense 33 are the hidden layers, Out is the output layer. The model is obtained as a matrix of vectors of numbers. When processing a table in natural language, vectors are extracted from tabular data to reflect the various parameters that affect yield.
The Embedding layer (embedding) converts objects into vectors. Vectors are data features expressed numerically, as neural networks work only with numeric vectors. In the embedding matrix, each index of the object and its translation into a vector are linked. Thus, we get a vector matrix.
Weight measurement is tracked for input data to the neural network.
The Embedding layer receives the data and at the output converts the data into vectors and transfers it to the following layer:
Input data1 = [X11,X12,X13,X14].
Input data2 = [X21,X22,X23,X24].
Input data3 = [X31,X32,X32,X34].
Input data4 = [Xn1, Xn2, Xn2, Xn4].
Weights1 = [W11,W21,W31,Wn1].
Phosphorus Nitrogen
Aver.air temp Humidity Precipitatio
n
Embedding Dense 32 Dense 33 Out
W11
W12
W13
W21 W22
W23
W31
W32
W33
Х1
Х2
Х3
Q1
Q2 Y1
Q3
W21
Х31
Х32
Х43
Q31
Q32 Y31
Q33
W21
Х21
Х22
Х23
Q21
Q22 Y21
Q23
115 Weights 2 = [W21,W22,W32].
Weights 3 = [W13,W23,W33].
The resulting vectors for learning are divided into two parts – training data and test data. The learning sample is used to train a model, the test sample is used to evaluate the predictive properties of the model on new data. The training sample and test sample share can usually be 50/50, 70/30 or 80/20, depending on the task. If the test_size coefficient is not specified, then by default the test sample takes 20% of the total sample.
The problem specifies the size of the test sample, respectively, by default the test sample size is taken from 100% of the data, and the remaining input data goes to the learning sample.
It is not necessary to have any parameters to split the input data into test and training data, the data is packaged, and the packages are shared at random. The test subset of vectors and their labels are stored in x_test variables (for vectors), y_test (for labels of all data in x_test). The learning subset of vectors is stored in x_train (for learning vectors), y_train (for labeling all data from x_train). Therefore, matrices of table type 3, 4, 5, 6 are obtained. The test sample is used in neural network training. The share of training and test samples is determined by the method of selection at work.
Tables from 3 to 6 show matrices with data below.
Table 3 – Matrix with data in x_test
0 1 2 3 .. … … 126 127 128 129
0 0 0 0 … … … 0 0 101 1
0 0 0 0 … … … 186 1 187 1
0 0 0 0 … … … 255 1 256 1
0 0 0 0 … … … 2 1 25 1
0 0 0 0 … … … 5 1 197 1
Table 4 – Matrix with data in у_train
0 1 2 3 .. … … 126 127 128 129
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 1
0 1 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
Table 5 – Matrix with data in x_train
0 1 2 3 .. … … 126 127 128 129
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 1
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
Table 6 – Matrix with data in у_test
0 1 2 3 .. … … 126 127 128 129
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 1
0 0 0 0 … … … 0 0 0 0
0 0 0 0 … … … 0 0 0 0
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After receiving the vectors, the Dense 32 layer calculates the data, that is, each input data element is multiplied by its weight (weight), the resulting product (private forecasts) are summed, and the weighted sum of inputs (scalar product) is deduced by formulae (1) and (2):
(X1 ∗ W11) + (X2 ∗ W21) + (Xn ∗ Wn1) = Q1 (1) (X1 ∗ W12) + (X2 ∗ W22) + (Xn ∗ Wn2) = Q2 (2) The input vector matrix is multiplied by the weight vectors, then the input input scalar products are summed up with a b (default is 1):
Q_ini = ∑ 𝑥𝑛𝑖 𝑖∗ 𝑤𝑖𝑗+ b (3)
The activation function is applied to the resulting value:
Qj = f(Q_ini) (4)
where Q_ini is the argument of the activation function, and the result is sent to the next layer.
The data released by Dense 32 gets a third layer of Dense 33.
Figure 2 – Hidden layers
The Dense 33 layer implements the output operation, is a fully connected layer, that is, the layer has a small dimension.
In this layer the weighted sum of the output layer is calculated and the Relu activation function is implemented.
In turn, incoming signals in Dense 33 are calculated in the same way as in other layers:
у_ini = ∑ 𝑦𝑛𝑖 𝑖∗ 𝑤𝑖𝑗+ b, (5)
to which the activation function applies:
yj = f(y_ini) (6)
Keras has implemented the following optimizers: SGD (Stochastic Gradient Descent), Adagrad, Adadelta, Adam (Adaptive Moment Estimation) and others. An optimiser is an algorithm that is used for iterative updating of the weights of a network based on training data. The method calculates individual adaptive learning speeds for different parameters. One technique commonly used in neural networks is the SGD optimizer (stochastic gradient downhill), which eliminates redundancy and updates for each training example. Adagrad is an algorithm that adapts learning speed to parameters. It performs fewer updates for parameters of common functions, produces more updates for parameters of rare functions and is often used
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with sparse data. Adam was introduced by D. King and D. Adam in 2014 [23]. Adam optimizer was used in this study: adam = Adam(0.0003). The learning rate with this optimizer was chosen to be 0.0003, which has shown the best execution of 0.5648.
Discussion and results
Thus, the construction of a neural network that has four layers was considered. Each layer performs calculations with the resulting vectors and produces a weighted sum to which the scalar value of the offset is added, and the activation function is applied to their result. The activation function is Relu.
The four-layer multi-layered neural network in the programme is presented as follows:
model.add(Embedding(n_most_common_param,emb_dim,input_length=X_train.shape[1])) model.add (Dense(100, activation='relu', input_dim=X_train.shape[1])
model.add (Dense(1)
According to the results of training using the built model based on SequentialKeras, the training accuracy is shown in Figure 3. Prediction [[205.84314696 550.50957937 10.7725984 ]] Phosphorus 205.84314695915165 Nitrogen 550.5095793667042 Yield 10.7725984.
Figure 3 – Neural network training accuracy
Obtaining such accuracy (Figure 3) is made possible by better preparation and pre-processing of the input data (Table 2), as well as the addition of selected hidden layers.
Thus, a neural network model was developed to predict the influence of phosphorus on spring wheat yield. Further studies may add additional hidden layers or apply other approaches for comparison. The proposed neural network model can be applied in research on prediction of influence of external factors on other similar cultures.
Conclusion
Predicting and improving the crop yield is important in agriculture. As a result of the work, solving the tasks led to the achievement of the the aim of the study.
So for data training, input data was prepared, which included the normalization process. The input data was processed using the robo method and converted into vectors.
As a result, a neural network model was developed to predict the influence of phosphorus on the yield of spring wheat, which has high accuracy.
In future, the results of this study can be applied in the next step – it is the development of an analytical system to predict the influence of external factors on the growth of wheat and other crops.
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ЖАЗДЫҚ БИДАЙ ӨНІМДІЛІГІНЕ ФОСФОРДЫҢ ӘСЕРІН БОЛЖАУ ҮШІН НЕЙРОНДЫҚ ЖЕЛІ МОДЕЛІН ЖАСАУ
С.Е. Шарипова*, А.С. Аканова
С. Сейфуллин атындағы Қазақ агротехникалық университеті, Астана, Қазақстан
E-mail: [email protected], [email protected]
Аңдатпа. Мақала жаздық бидай өнімділігіне фосфордың әсерін болжау мәселесіне арналған. Бұл зерттеудің мақсаты-жаздық бидай өнімділігіне фосфордың әсерін болжауға арналған нейрондық желі моделін жасау. Автор Қазақстан Республикасының агроөнеркәсіптік кешені кәсіпорындарында ақпараттық технологияларды, жасанды интеллектті пайдалану сияқты мәселелерді қозғайды.
Мақала аясында осы саладағы қолданыстағы зерттеулерге шолу жасалды. Сондай-ақ, жаздық бидайдың өнімділігіне фосфордың әсерін болжауға арналған нейрондық желінің жоқ екендігі анықталды.
Осыған байланысты соңғы 24 жылдағы жаздық бидайдың өнімділігі және фосфор мен азотты енгізу бойынша деректер жиналды, олар кейіннен жаздық бидайдың өнімділігіне фосфордың әсерін болжауға бағытталған нейрондық желіні оқыту үшін пайдаланылды.
Осы зерттеу аясында құрамында фосфор мен азот бар тыңайтқыштарды қолдану, ауаның орташа температурасы, ылғалдылық, жауын-шашын сияқты параметрлердің 1997 жылдан 2021 жылға дейінгі бидай өнімділігіне әсері қарастырылған. Робастық әдіспен кіріс деректеріне алдын-ала өңдеу жүргізілді, содан кейін олар векторларға ауыстырылды, одан соң 4 қабаттан тұратын нейрондық желі моделі жасалды: Embedding, Dense32, Dense33 және Out. Нейрондық желіні құру үшін Python – Keras кітапханасы пайдаланылды.
Мақаланы ауыл шаруашылығындағы болжау зерттеушілері, агрономдар және IT мамандары пайдалана алады.
Түйін сөздер: нейрондық желілер, болжау, өнімділік, деректерді қалыпқа келтіру, жаздық бидай.
РАЗРАБОТКА МОДЕЛИ НЕЙРОННОЙ СЕТИ ДЛЯ
ПРОГНОЗИРОВАНИЯ ВЛИЯНИЯ ФОСФОРА НА УРОЖАЙНОСТЬ ЯРОВОЙ ПШЕНИЦЫ
С.Е. Шарипова*, А.С. Аканова
Казахский агротехнический университет имени С. Сейфуллина, Астана, Казахстан
E-mail: [email protected], [email protected]
Аннотация. В статье рассматривается вопрос о прогнозировании влияния фосфора на урожайность яровой пшеницы. Цель данного исследования – разработать модель нейронной сети для прогнозирования влияния фосфора на урожайность яровой пшеницы. Автор затрагивает вопросы использования информационных технологий и искусственного интеллекта на предприятиях агропромышленного комплекса Республики Казахстан.
В рамках данной статьи был проведен обзор существующих работ в исследуемой области. А также была выявлена проблема отсутствия нейронной сети для прогнозирования влияния фосфора на урожайность яровой пшеницы. В связи с этим были собраны данные по внесению фосфора и азота, по урожайности яровой пшеницы за последние 24 года, которые в дальнейшем были использованы для обучения нейронной сети, целью которой является прогнозирование влияния фосфора на урожайность яровой пшеницы.
В рамках данного исследования рассматривается влияние различных параметров (внесение удобрений из фосфора и азота, средняя температура воздуха, влажность, осадки) на урожайность пшеницы с 1997 года по 2021 год. Была проведена предварительная обработка входных данных с помощью робастного метода, которые затем были переведены в векторы. Впоследствии была разработана модель нейронной сети,
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состоящая из 4 слоев: Embedding, Dense32, Dense33 и Out. Для создания нейронной сети была использована библиотека Python – Keras. Статья может быть использована исследователями в области прогнозирования в сельском хозяйстве, агрономами и специалистами IT.
Ключевые слова: нейронные сети, прогнозирование, урожайность, нормализация данных, яровая пшеница.
Басылымның шығыс деректері
Мерзімді баспасөз басылымының атауы «Алматы энергетика және байланыс университетінің Хабаршысы» ғылыми- техникалық журналы
Мерзімді баспасөз басылымының меншік иесі «Ғұмарбек Дәукеев атындағы Алматы энергетика және байланыс университеті»
коммерциялық емес акционерлік қоғамы, Алматы, Қазақстан
Бас редактор Профессор, т.ғ.к., В.В. Стояк
Қайта есепке қою туралы куәліктің нөмірі мен күні және берген органның атауы
№ KZ14VPY00024997, күні 17.07.2020,
Қазақстан Республикасының Ақпарат және қоғамдық даму министрлігі
Мерзімділігі Жылына 4 рет (тоқсан сайын)
Мерзімді баспасөз басылымының реттік нөмірі және жарыққа шыққан күні
Жалпы нөмір 60, 1-басылым, 2023 жылғы 31 наурыз
Басылым индексі 74108
Басылым таралымы 200 дана
Баға Келісілген
Баспахана атауы, оның мекен-жайы «Ғұмарбек Дәукеев атындағы Алматы энергетика және байланыс университеті»
КЕАҚ баспаханасы, Байтұрсынұлы көшесі, 126/1 үй, А120 каб.
Редакцияның мекен-жайы 0 5 0 0 1 3 , Алм а т ы қ. , «Ғ ұ м а р бе к Дә ук е ев а т ы н да ғы А л м а т ы эн ер г ет и ка ж ә н е ба й ла н ы с ун и в ер с и т ет і » К ЕА Қ, Б а й т ұ р с ы н ұ лы к- с і , 1 2 6 / 1 ү й , ка б. А 2 2 4 , т е л. : 8 ( 7 2 7 ) 2 9 2 5 8 4 8 , 7 08 8 8 0 7 7 9 9 , e - m a i l : v e s t n i k @ a u e s . k z
Выходные данные
Название периодического печатного издания Научно-технический журнал «Вестник Алматинского университета энергетики и связи»
Собственник периодического печатного издания
Некоммерческое акционерное общество «Алматинский университет энергетики и связи имени Гумарбека Даукеева», Алматы, Казахстан
Главный редактор Профессор, к.т.н., Стояк В.В.
Номер и дата свидетельства о постановке на переучет и наименование выдавшего органа
№ KZ14VPY00024997 от 17.07.2020
Министерство информации и общественного развития Республики Казахстан
Периодичность 4 раза в год (ежеквартально)
Порядковый номер и дата выхода в свет
периодического печатного издания Валовый номер 60, выпуск 1, 31 марта 2023
Подписной индекс 74108
Тираж выпуска 200 экз.
Цена Договорная
Наименование типографии, ее адрес Типография НАО «Алматинский университет энергетики и связи имени Гумарбека Даукеева», ул. Байтурсынулы, дом 126/1, каб. А 120
Адрес редакции 050013, г. Алматы, НАО «Алматинский у ниверситет э нергетики и с вязи имени Гумарбека Даукеева», ул. Байтурсынулы, дом 126/1, каб. А 224, т ел.: 8 (727) 292 58 48, 708 880 77 99, e-mail: [email protected]
Issue output
Name of the periodical printed publication Scientific and technical journal "Bulletin of the Almaty University of Power Engineering and Telecommunications"
Owner of the periodical printed publication Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev", Almaty, Kazakhstan
Chief Editor Professor, candidate of technical sciences Stoyak V.V.
Number and date of the registration certificate and the name of the issuing authority
№ KZ14VPY00024997 from 17.07.2020
Ministry of Information and Social Development of the Republic of Kazakhstan
Periodicity 4 times a year (quarterly)
Serial number and date of publication of a periodical printed publication
Number 60, edition 1, March 31, 2023
Subscription index 74108
Circulation of the issue 200 copies
Price Negotiable
The name of the printing house, its address Printing house of Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev", 126/1 Baitursynuly str., office A 120, Almaty, Republic of Kazakhstan
Editorial office address 050013, Non-profit joint-stock company "Almaty University of Power Enginnering and Telecommunications named after Gumarbek Daukeyev",
A 2 2 4 , t e l .: 8 (727) 292 58 48, 708 880 77 99, e-mail: [email protected]
