Exploratory Data Analysis and Kernel Feature Fusion for Enhanced SVM and Random Forest–Based Crop Recommendation

Year 2025, Volume: 9 Issue: Special, 9 - 10

Kutalmış Turhal , Ümit Çiğdem Turhal

https://doi.org/10.31015/2025.si.5

Abstract

Modern crop recommendation systems must accurately grasp the complex and nonlinear relationships between soil nutrients to support effective agricultural decisions. In this study, we introduce a framework that combines supervised and unsupervised learning through kernel feature fusion, integrating Radial Basis Function (RBF) Kernel Principal Component Analysis (KPCA) and Kernel Linear Discriminant Analysis (KLDA) into a single seven-dimensional embedding. First, six principal components are extracted using RBF-KPCA to capture global nonlinear variance in the raw data. Similarly, in the raw space, an Nystroem-approximated RBF transformation followed by LDA produces a single discriminant axis (KLDA) for better supervised class separation. These features are fused by concatenation and then input into Support Vector Machine (SVM) classifiers (using polynomial and RBF kernels) and a Random Forest (RF) classifier. In the experiments, a publicly available dataset comparing maize and barley based on six soil features was used. The fused representation significantly outperformed raw data and single-embedding methods, with Polynomial SVM increasing by 18.5%, RBF SVM improving by 10.1%, and RF rising by 4.7% over the raw data. These results show that combining unsupervised variance maximization with supervised discriminant projection creates a richer, more discriminative feature space—especially beneficial for SVMs in crop recommendation tasks. Our kernel fusion approach offers a powerful and flexible strategy for precision agriculture, enabling robust decision support without extensive field trials or repeated laboratory tests.

Keywords

Precision Agriculture , Crop Recommendation , Soil Nutrient Embedding , Kernel Feature Fusion , Artificial Intelligence

References

• Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Computer Science Review, 40, 100378.
• Bandara, P., Weerasooriya, T., Ruchirawya, T., Nanayakkara, W., Dimantha, M., & Pabasara, M. (2020). Crop recommendation system. International Journal of Computer Applications, 975, 8887.
• Barburiceanu, S., Meza, S., Orza, B., Malutan, R., & Terebes, R. (2021). Convolutional neural networks for texture feature extraction. Applications to leaf disease classification in precision agriculture. IEEE Access, 9, 160085-160103.
• Baudat, G., & Anouar, F. (2000). Generalized discriminant analysis using a kernel approach. Neural computation, 12(10), 2385-2404.
• Briscik, M., Dillies, M. A., & Déjean, S. (2023). Improvement of variables interpretability in kernel PCA. BMC bioinformatics, 24(1), 282.
• Boppudi, S., & Jayachandran, S. (2024). Improved feature ranking fusion process with Hybrid model for crop yield prediction. Biomedical Signal Processing and Control, 93, 106121.
• Cheng, X. (2024). A comprehensive study of feature selection techniques in machine learning models. Available at SSRN 5154947.
• Çağlar, E. (2024). The impact of sectors on agriculture based on artificial intelligence data: a case study on G7 countries and Turkiye. International Journal of Agriculture Environment and Food Sciences, 8(3), 486-494.
• Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024(1), 2126734.
• Gosai, D., Raval, C., Nayak, R., Jayswal, H., & Patel, A. (2021). Crop recommendation system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(3), 558-569.
• Guan, H., Ren, Y., Tang, H., & Xiang, J. (2024). Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects. Measurement Science and Technology, 35(8), 082001.
• Gunawan, M. I., Sitopu, J. W., Sechan, G., & Gunawan, I. (2024). Optimizing Crop Selection: A Multi-Criteria Decision Support System for Sustainable Agriculture. International Journal of Enterprise Modelling, 18(3), 113-123.
• Gür, Y. E. (2024). Innovation in the dairy industry: forecasting cow cheese production with machine learning and deep learning models. International Journal of Agriculture Environment and Food Sciences, 8(2), 327-346.
• Hekmatmanesh, A., Wu, H., Jamaloo, F., Li, M., & Handroos, H. (2020). A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications. Multimedia Tools and Applications, 79(25), 17521-17549.
• Huang, X., Wang, H., & Li, X. (2024). A multi-scale semantic feature fusion method for remote sensing crop classification. Computers and Electronics in Agriculture, 224, 109185.
• Ibrahim, S. A. (2022). Improving land use/cover classification accuracy from random forest feature importance selection based on synergistic use of sentinel data and digital elevation model in agriculturally dominated landscape. Agriculture, 13(1), 98.
• Jiao, L., Xie, C., Chen, P., Du, J., Li, R., & Zhang, J. (2022). Adaptive feature fusion pyramid network for multi-classes agricultural pest detection. Computers and electronics in agriculture, 195, 106827.
• Kahya, E., & Aslan, Y. (2024). Detection of artichoke on seedling based on YOLOV5 model. International Journal of Agriculture Environment and Food Sciences, 8(1), 186-201.
• Kaushal, S., Nayi, P., Rahadian, D., & Chen, H. H. (2022). Applications of electronic nose coupled with statistical and intelligent pattern recognition techniques for monitoring tea quality: A review. Agriculture, 12(9), 1359.
• Kempfert, K. C., Wang, Y., Chen, C., & Wong, S. W. (2020). A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification. Intelligent Data Analysis, 24(2), 267-290.
• Kusuma, C. G., Dharumarajan, S., Vasundhara, R., Gomez, C., Manjunatha, M. H., & Hegde, R. (2025). Predicting Soil Nutrient Classes Using Vis–NIR Spectroscopy to Support Sustainable Farming Decisions. Land Degradation & Development.
• Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
• Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., ... & Lucat, L. (2021). Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy, 11(6), 1227.
• Mahesh, T. R., Thakur, A., Velmurugan, A. K., Khan, S. B., Gadekallu, T. R., Alzahrani, S., & Alojail, M. (2024). AgriFusion: A Low‐Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation. Computational Intelligence, 40(6), e70006.
• Mishra, H., & Mishra, D. (2024). AI for data-driven decision-making in smart agriculture: From field to farm management. In Artificial Intelligence Techniques in Smart Agriculture (pp. 173-193). Singapore: Springer Nature Singapore.
• Peng, P., & Zhao, Y. P. (2023). Robust semi-supervised discriminant embedding method with soft label in kernel space. Neural Computing and Applications, 35(11), 8601-8623.
• Piccialli, V., & Sciandrone, M. (2022). Nonlinear optimization and support vector machines. Annals of Operations Research, 314(1), 15-47.
• Ruano-Ordás, D. (2024). Machine learning-based feature extraction and selection. Applied Sciences, 14(15), 6567.
• Swaminathan, B., Palani, S., & Vairavasundaram, S. (2023). Feature fusion based deep neural collaborative filtering model for fertilizer prediction. Expert Systems with Applications, 216, 119441.
• Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12(5), 91.
• Upadhyay, N., Sharma, D. K., & Bhargava, A. (2025). 3SW-Net: A Feature Fusion Network for Semantic Weed Detection in Precision Agriculture. Food Analytical Methods, 1-17.
• Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York.
• Yanardağ, A. B. (2025). Soil chemistry in apricot cultivation: evaluation of C and N dynamics by PCA and correlation analysis. International Journal of Agriculture Environment and Food Sciences, 9(2), 360-373.
• Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020). A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends, 1(1), 56-70.