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Talk · International Conference on Communications, Information, Electronic and Energy Systems · November 23, 2023

An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

Aigul Mimenbayeva, Maylen Omirtay, Rozamgul Niyazova, Gulmira Bekmagambetova, Raya Suleimenova, Ainur Tursumbayeva

Aigul Mimenbayeva
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Abstract

The paper analyzes and predicts the yield of grain crops of LLP “North-Kazakhstan Agricultural Experimental Station” for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development.

Smart Agriculture and AINeural Networks and ApplicationsForecasting Techniques and Applicationsartificial neural networksmultilayer perceptronagricultural production monitoring

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