Home⇛Journal of Interdisciplinary Perspectives⇛vol. 3 no. 8 (2025)

The Analysis of Crop Production Data for Predicting Future Trends of Highland Vegetables in Benguet

Anna Rhodora M. Quitaleg | Menchita F. Dumlao

Discipline: Agriculture

 

Abstract:

This study employs machine learning techniques to enhance agricultural prediction to secure future food supplies and support sustainable practices. It aims to forecast trends in highland vegetable crops in Benguet, providing invaluable insights for enhancing yields and contributing to vital food security initiatives. Notably, the researchers concentrated on crop production data due to inconsistencies and incompleteness in the historical datasets collated. Furthermore, it proposes a mobile platform for data collection from farmers, capturing their production and farming practices. This method intends to streamline the data collection process, enabling LGUs to collate and utilize the information effectively. The methodology employed a structured approach, encompassing data collection, preparation, analysis, modeling, and evaluation using Python programming and Power BI tools. In the analytical phase, a variety of machine learning models were explored, including Linear Regression, Lasso Regression, Ridge Regression, Decision Trees, SVM, and Random Forest. Model evaluation was based on the R², MAE, MSE, and RMSE. The Random Forest model emerged as the most suitable choice, boasting the best metrics for production purposes with an R² of 99%. The outcomes of this study hold significant potential, not only in reshaping agricultural practices and decision-making but also in fostering sustainable approaches to highland vegetable cultivation.



References:

  1. Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine learning applications in agriculture: Current trends, challenges, and future perspectives. Agronomy, 13(12), 2976. https://doi.org/10.3390/agronomy13122976
  2. Bali, N., & Singla, A. (2022). Emerging trends in machine learning to predict crop yield and study its influential factors: A survey. Archives of Computational Methods in Engineering, 29(1), 95–112. https://doi.org/10.1007/s11831-021-09569-8 
  3. Cabello-Solorzano, K., Ortigosa De Araujo, I., Peña, M., Correia, L., & Tallón-Ballesteros, A. J. (2023). The impact of data normalization on the accuracy of machine learning algorithms: A comparative analysis. In P. García Bringas, H. Pérez García, F. J. Martínez De Pisón, F. Martínez Álvarez, A. Troncoso Lora, Á. Herrero, J. L. Calvo Rolle, H. Quintián, & E. Corchado (Eds.), 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (Vol. 750, pp. 344–353). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42536-3_33 
  4. Colombo-Mendoza, L. O., Paredes-Valverde, M. A., Salas-Zárate, M. d. P., & Valencia-García, R. (2022). Internet of things-driven data mining for smart crop production prediction in the peasant farming domain. Applied Sciences, 12(4), 1940. https://doi.org/10.3390/app12041940 
  5. de Bruin, G. J., Veenman, C. J., van den Herik, H. J., & Takes, F. W. (2021). Experimental evaluation of train and test split strategies in link prediction. In R. M. Benito, M. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex Networks 2020 (pp. 79–91). Springer. https://doi.org/10.1007/978-3-030-65351-4_7 
  6. de Melo Barros Junior, P. R., Bunge, K. L., Serravalle Reis Rodrigues, V. H., Ribeiro, L. M., Lima, A. G. B., Lima, G. B., & Manso, M. (2022). Multi-fractal detrended cross-correlation heatmaps for time series analysis. Scientific Reports, 12, 21655. https://doi.org/10.1038/s41598-022-26207-w 
  7. Food and Agriculture Organization. (2021). The state of food security and nutrition in the world 2021. https://www.fao.org/3/cb4474en/cb4474en.pdf 
  8. GeeksforGeeks. (2023). Data mining techniques. https://www.geeksforgeeks.org/data-mining-techniques/ 
  9. GeeksforGeeks. (2023a). How to create a seaborn correlation heatmap in Python? https://tinyurl.com/4jkff5yu 
  10. Gupta, S. K., & Malik, S. (n.d.). Application of predictive analytics in agriculture. Technoarete Transactions on Intelligent Data Mining and Knowledge Discovery. https://tinyurl.com/2p9neup4 
  11. Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., & Kim, S. H. (2016). Random forests for global and regional crop yield predictions. PLOS ONE, 11(6), e0156571. https://doi.org/10.1371/journal.pone.0156571 
  12. Kumar, V., & Malik, M. L. (2018). Predictive analytics: A review of trends and techniques. International Journal of Computer Applications, 182(1), 31–37. https://doi.org/10.5120/IJCA2018917434 
  13. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674 
  14. Lopez-Ridaura, S., Barba-Escoto, L., Reyna, C., Hellin, J., Gerard, B., & van Wijk, M. (2019). Food security and agriculture in the Western Highlands of Guatemala. Food Security, 11(4), 817–833. https://doi.org/10.1007/s12571-019-00940-z 
  15. Mishra, P., Khan, R., & Baranidharan, B. (2020). Crop yield prediction using gradient boosting regression. International Journal of Innovative Technology and Exploring Engineering, 9(3), 2293–2297. https://doi.org/10.35940/ijitee.C8879.019320 
  16. Quitaleg, A. R., & Dumlao, M. (2023). A decision support system for Benguet’s upland vegetable crop prediction using machine learning techniques. European Chemical Bulletin. https://tinyurl.com/y8c68ndj  
  17. Republic of the Philippines. (2019). Republic Act No. 11293: Philippine Innovation Act. https://lawphil.net/statutes/repacts/ra2019/ra_11293_2019.html 

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic