Issue |
BIO Web Conf.
Volume 130, 2024
International Scientific Conference on Biotechnology and Food Technology (BFT-2024)
|
|
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Article Number | 01011 | |
Number of page(s) | 8 | |
Section | Plant Biotechnology | |
DOI | https://doi.org/10.1051/bioconf/202413001011 | |
Published online | 09 October 2024 |
Crop yield forecasting using neural networks trained on the basis of agrometeorological and agrochemical data
1 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
2 Bauman Moscow State Technical University, 105005 Moscow, Russia
* Corresponding author: sofaglu2000@mail.ru
In this study, a neural network model was developed and investigated for predicting crop yields based on data on weather conditions, the use of fertilizers and the content of basic nutrients in the soil (nitrogen, phosphorus and potassium). The research is based on the use of a multilayer perceptron architecture with Rely activation functions for hidden layers and linear activation for the output layer. The evaluation of the model quality was carried out using the mean square error (MSE), which was 0.5783 in the test sample, demonstrating high accuracy of predictions. Visualization of the results included analysis of scatter plots, residuals, histograms of residuals and comparison of distributions of actual and predicted values. The results obtained confirm the effectiveness of the proposed model for yield forecasting tasks, which makes it a valuable tool for optimizing agricultural production.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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