Hybrid Recommender System for Precision Chemical Application in Banana Cultivation Using Matrix Factorization and Content-Based Filtering

Proper management of pesticides and fertilizers is critical towards effective control of the banana diseases, but integration of various agricultural data has been a problem. The novelty of this study is the hybrid recommendation system which encompasses Content-Based Filtering (CBF) with Matrix Factorization (MF) to be used when recommending chemical treatment of bananas during cultivation. The system exploits the use of heterogenous data- such as soil nutrient profiles (NPK, pH), climatic variables, and disease signatures to create customized chemical recommendation to manage the disease. A real-world agricultural dataset was used in the evaluation of the hybrid approach and the improvement, precision, recall, F1-score, and the accuracy of the system were measured. The findings indicate that the suggested model performed better than the traditional models of single-method or user-based recommendation systems and predicted the disease outbreak with high accuracy (F1-score) up to 98 percent in Black Sigatoka; these results were highly consistent across other disease classes and different chemical interventions. Notably, the hybrid system helps not only to optimize the costs of chemical use and crop yields, but also to create the environmental sustainability by reducing the number of the superfluous chemical use. Methodology, the characteristics of the dataset and the measures that have been employed are described, which explains how CBF and MF integration solve the complexity and variability in agricultural data. The solution provided in this work is a high-performance scalable tool in precision agriculture, which assists further in the informed decision-making of the farmer and agricultural planners.

@article{Tirandasu2025,
  title     = {Hybrid Recommender System for Precision Chemical Application in Banana Cultivation Using Matrix Factorization and Content-Based Filtering},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ravi Kumar Tirandasu and Prasanth Yalla and Pachipala Yellamma},
  doi       = {10.14569/IJACSA.2025.0160891},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160891}
}