Keywords:-
Article Content:-
Abstract
Artificial Intelligence (AI) has significantly transformed the agricultural sector by enhancing crop yield forecasting, decision-making, and the analysis of soil and environmental conditions. Consequently, many studies have adopted deep learning (DL) techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for crop yield prediction. CNNs are effective in extracting spatial features from input data, while LSTMs capture temporal and phenological patterns, both of which are critical for accurate yield estimation. This study proposes a novel hybrid framework for cassava yield prediction that integrates CNN, LSTM, and Harris Hawk Optimisation (HHO). Soil and meteorological datasets were obtained from SoilGrids250m version 2.0 and the Nigerian Meteorological Agency (NIMET), Abuja, respectively, while cassava yield data were sourced from the Food and Agriculture Organisation Statistics. The datasets were preprocessed through noise removal, normalisation, and validation using a 5-fold cross-validation technique. The proposed HHO-CNN-LSTM model was evaluated against a conventional CNN-LSTM model using R-square (R²), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results showed that the CNN-LSTM model performed poorly, with R² = −0.56726, MAE = 1.431207, MSE = 2.050261, RMSE = 1.431075, and MAPE = 13.54528. In contrast, the HHO-CNN-LSTM model achieved superior performance, recording R² = 0.370277, MAE = 0.904463, MSE = 0.82379, RMSE = 0.907629, and MAPE = 8. 745989.These findings demonstrate that integrating HHO significantly improves prediction accuracy and model reliability, making the proposed approach a valuable decision-support tool for sustainable cassava production and agricultural resilience.
References:-
References
Al-Adhaileh, M. H., and Aldhyani, T.H.H. (2022). Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia. PeerJ Computer Science, 8: 1-24. https://pmc.ncbi.nlm.nih.gov/articles/PMC9575863/pdf/peerj-cs-08-1104.pdf
Ali, T., Rehman, S.U., Ali, S., Mahmood, K., Obregon, S.A., Iglesias, R.C., Khurshaid, T. and Ashraf, I. (2024). Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops. Scientific Reports, 14(30062): 1-16.
Alibabaei, K., Gaspar, P.D. and Lima, T.M. (2021). Crop yield estimation using deep learning based on climate big data and irrigation scheduling. Energies, 14(11): 1-21. https://www.mdpi.com/1996-1073/14/11/3004/pdf
Ansarifar, J., Wang, L. and Archontoulis, S. V. (2021). An interaction regression model for crop yield prediction. Scientific Reports, 11(1): 1-14. https://www.nature.com/articles/s41598-021-97221-7.pdf
Chang, Y., Latham, J., Licht, M. and Wang, L. (2023). A data-driven crop model for maize yield prediction. Communication Biology,6: 1-9. https://pmc.ncbi.nlm.nih.gov/articles/PMC10121691/pdf/42003_2023_Article_4833.pdf
Challa, S. K., Kumar, A. and Semwal, V. B. (2021). A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. The Visual Computer, 38(12): 1-15.
Choubey, M., Gupta, Y.K., Dubey, A. and Prasad, R. (2025). Deep learning for precision agriculture: Predicting crop yields in a changing climate. In book: AgricTech Revolution, pp. 253-275.
https://www.researchgate.net/publication/396927034_Deep_Learning_for_
Precision_Agriculture_Predicting_Crop_Yields_in_a_Changing_Climate
Edamisan, S. I. and Kemisola, A. (2020). Cassava production in Nigeria: trends, instability and decomposition analysis (1970-2018). Heliyon, 6 (10): 1-10.
Eiben, A. E. and Smith, J. E. (2015). Introduction to evolutionary computing (2nd ed.). Springer. Pp. 1-294.
FAOASTAT, F. (2019). Food and agriculture data.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97: 849-872.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8): 1735-1780. https://www.bioinf.jku.at/publications/older/2604.pdf
Javaregowda, M., Indiramma, R. and Nagaraju, N. (2025). M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction. Multimedia Tools and Applications, 84(32): 39787-39811.
Kamilaris, A. and Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147(1): 70-90.
Khaki, S., Wang, L. and Archontoulis, S.V. (2020). A CNN-RNN framework for crop
yield prediction. Frontiers in Plant Science, 10(1750): 1-14. https://public-pages-files-2025.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.01750/pdf
Khaki, S., and Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science. 10(621): 1-10. https://doi.org/10.3389/fpls.2019.00621
LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Probert, M. (2012). Engineering optimisation: An introduction with metaheuristic applications, by Xin-She Yang, Contemporary Physics, 53(3): 271-272.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8): 1-29. https://www.mdpi.com/1424-8220/18/8/2674/pdf
Srivastava, A. K., Ewert, F., Akinwumiju, A. S., Zeng, W., Ceglar, A., Ezui, K. S., Adelodun, A., Adebayo, A., Sobamowo, J., Singh, M., Rahimi, J. and Gaiser, T. (2023). Cassava yield gap-A model-based assessment in Nigeria. Frontiers in Sustainable Food Systems, 6: 1-20. https://public-pages-files-2025.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2022.1058775/pdf
Taremwa,, D., Ahishakiye, E., Obbo, A., Kisozi, P.K. and Kaggwa, F. (2025). Prediction of maize yield in Uganda using CNNLSTM architecture on a multimodal climate and
remote sensing dataset. Research Square, 1-32.
Wang, H.H., Wang, Y.C., Wee, B.L., Yan, J.K., Yahya, F.B. (2024). Optimizing crop yield prediction: A hybrid approach integrating CNN and LSTM networks. Journal of Theoretical and Applied Information Technology, 102(22): 8075-8083.
Wang, J., Jiang, W., Li, Z. and Lu, Y. (2021). A new multi-scale sliding window LSTM framework (MSSW-LSTM): A case study for GNSS time-series prediction. Remote Sensing, 13(16): 1-16. http://dx.doi.org/10.3390/rs13163328
Xie, H., Zhang, L. and Lim, C. P. (2020). Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer. https://core.ac.uk/download/pdf/334954582.pdf
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver Press.
Zhou, W., Song, C., Liu, C., Fu, Q., An, T., Wang, Y., Sun, X., Wen, N.,Tang, H. and Wang, Q. (2023). A prediction model of maize field yield based on the fusion of multitemporal and multimodal UAV data: A case study in Northeast China. Remote Sensing, 15(3483): 1-22.