CNN-RNN Hybrid Model for Predicting Agricultural Yield from Soil Physico-Chemical Parameters
Evariste Kantshia Bakatubia *
Faculty of Science and Technology, Department of Mathematics and Computer Science, National Pedagogical University (UPN), Kinshasa- Democratic Republic of Congo.
Pascaline Kizodisa Mbilankazi
Department of Computer Science, Sciences and Technology, Higher Pedagogical Institute of Mbanza-Ngungui (ISP/MBANZA), Kinshasa, Democratic Republic of the Congo.
Fortunat Tshimanga Mbuyi
Faculty of Science and Technology, Department of Mathematics and Computer Science, National Pedagogical University (UPN), Kinshasa- Democratic Republic of Congo.
Christian Ntumba Cinema
Faculty of Science and Technology, Department of Mathematics and Computer Science, National Pedagogical University (UPN), Kinshasa- Democratic Republic of Congo.
Charles Djamba Pongembe
Faculty of Science and Technology, Department of Mathematics and Computer Science, National Pedagogical University (UPN), Kinshasa- Democratic Republic of Congo.
Pierre Kamuina Kambayi
Faculty of Science and Technology, Department of Mathematics and Computer Science, University of Kinshasa, Kinshasa- Democratic Republic of Congo.
Pierre Kafunda Katalay
Faculty of Science and Technology, Department of Mathematics and Computer Science, University of Kinshasa, Kinshasa- Democratic Republic of Congo.
*Author to whom correspondence should be addressed.
Abstract
Smart agriculture represents an important challenge for food security and the sustainable optimisation of agricultural production systems. However, accurate agricultural yield prediction remains complex because of the nonlinear interactions among soil physicochemical parameters, environmental conditions and crop development over time. In this context, this study proposes a hybrid deep learning model that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs) to improve agricultural yield prediction from soil physicochemical data.
The adopted methodology is based on an experimental approach that integrates agricultural data collection, time-series pre-processing, variable normalisation and the fusion of spatial and temporal features within a hybrid CNN-RNN architecture. The variables considered include soil pH, nitrogen, phosphorus, potassium, organic matter, moisture and selected climatic data. Model performance was evaluated using the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²), and was compared with that of classical models, including linear regression, random forests and support vector machines (SVMs).
R2R2 The dataset, comprising 202 agricultural observations, was divided using an 80% training and 20% testing strategy to ensure an independent evaluation of model performance. The results show that the CNN-RNN hybrid model outperforms the traditional approaches, with an RMSE of 0.032, an MAE of 0.0221 and a coefficient of determination (R²) of 0.9895, indicating improved generalisation and higher accuracy in predicting agricultural yields. The proposed model also enables the identification of the soil parameters most influential in agricultural productivity, particularly pH and NPK nutrients.
This research shows that integrating hybrid deep learning techniques into precision agriculture can support the development of intelligent agricultural decision-support systems. It also provides a basis for the future integration of satellite data, IoT sensors and advanced artificial intelligence architectures in sustainable agriculture.
Keywords: Smart agriculture, deep learning, convolutional neural networks (CNNs), recurrent neural networks (RNNs/LSTMs), agricultural yield prediction, soil physicochemical parameters, artificial intelligence, precision agriculture, spatiotemporal analysis, CNN-RNN hybrid model