Leveraging Ensemble Learning Technique for Efficient Fertilizer Recommendation
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Des 31, 2025
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Agricultural productivity plays a critical role in improving crop yields, and the suitable use of fertilizers plays a significant role in enhancing crop yield. Traditional fertilizer recommendation approaches often rely on generalized strategies that may not account for discrepancies in soil properties, climatic conditions. To address this limitation, we proposed an intelligent Fertilizer Recommendation System (FRS) using an Ensemble Learning method. This system integrates multiple ensemble learning models, such as Bagging, AdaBoost, GBoosting, Extra Trees, and CatBoost to enhance recommendation accuracy. The ensemble model is trained on soil parameters (N) nitrogen (P) phosphorus, (K) potassium, and moisture to recommend the optimal fertilizer type in Andhra Pradesh region, India. The result shows that all ensemble models utilized were effective, and CatBoost model has achieved 94.78% with highest accuracy, when compared with the other ensemble models.
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Gimba, A. M., & Pradeep Kumar Mishra. (2025). Leveraging Ensemble Learning Technique for Efficient Fertilizer Recommendation. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(4), 363 - 371. https://doi.org/10.62527/jitsi.6.4.509
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[21] Sajindra, H., Abekoon, T., Jayakody, J.A.D.C.A., Rathnayake, U., 2024. A novel deep learning model to predict the soil nutrient levels (N, P, and K) in cabbage cultivation. Smart Agric. Technol. 7, 100395. https://doi.org/10.1016/j.atech.2023.100395
[22] Sunandini, M., Deepiga, R., Gokulapriya, A., 2024. SMART SOIL FERTILIZER MONITORING AND CROP RECOMMENDATION SYSTEM BY USING IOT AND MACHINE LEARNING TECHNOLOGY 06.
[23] Tanaka, T.S.T., Heuvelink, G.B.M., Mieno, T., Bullock, D.S., 2024. Can machine learning models provide accurate fertilizer recommendations? Precis. Agric. 25, 1839–1856. https://doi.org/10.1007/s11119-024-10136-x
[24] H. Amnur, Y. Syanurdi, R. Idmayanti, and A. Erianda, “Developing Online Learning Applications for People with Hearing Impairment,” JOIV : Int. J. Inform. Visualization, vol. 5, no. 1, pp. 32–38, Mar. 2021, doi: 10.30630/joiv.5.1.457.
[25] Thorat, T., Patle, B.K., Kashyap, S.K., 2023. Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming. Smart Agric. Technol. 3, 100114. https://doi.org/10.1016/j.atech.2022.100114
[26] Tkatek, S., Amassmir, S., Belmzoukia, A., Abouchabaka, J., 2023. Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region. Int. J. Electr. Comput. Eng. IJECE 13, 5942. https://doi.org/10.11591/ijece.v13i5.pp5942-5950
[27] Vijender Reddy, G., Venkata Krishna Reddy, M., Spandana, K., Subbarayudu, Y., Albawi, A., Chandrashekar, R., Singla, A., Praveen, 2024. Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system. E3S Web Conf. 507, 01078. https://doi.org/10.1051/e3sconf/202450701078
[28] Zahra, S., Sharma, S., Kumar, S., 2025. The Agri-IQ Revolution: Crop and Fertilizer Recommendations Tailored by Nature. https://doi.org/10.20944/preprints202503.0376.v1
[2] Basava, C., 2024. Optimizing fertilizer usage in agriculture with AI Driven Recommendations 21.
[3] Bhola, A., Kumar, P., 2024. ML-CSFR: A Unified Crop Selection and Fertilizer Recommendation Framework based on Machine Learning. Scalable Comput. Pract. Exp. 25, 4111–4127. https://doi.org/10.12694/scpe.v25i5.2599
[4] Bhosale, D.R.S., Kakad, M.T.P., Kakad, K.C., n.d. Smart Crop Prediction and Fertilizer Recommendation using Machine Learning.
[5] Bouni, M., Hssina, B., Douzi, K., Douzi, S., 2022. Towards an Efficient Recommender Systems in Smart Agriculture: A deep reinforcement learning approach. Procedia Comput. Sci. 203, 825–830. https://doi.org/10.1016/j.procs.2022.07.124
[6] D N, V., Choudhary, S., 2024. The new machine learning feature selection method used in fertilizer recommendation. Bull. Electr. Eng. Inform. 13, 3455–3462. https://doi.org/10.11591/eei.v13i5.7198
[7] Dhal, S.B., Bagavathiannan, M., Braga-Neto, U., Kalafatis, S., 2022. Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets. Artif. Intell. Agric. 6, 68–76. https://doi.org/10.1016/j.aiia.2022.05.001
[8] Durai, S.K.S., Shamili, M.D., 2022. Smart farming using Machine Learning and Deep Learning techniques. Decis. Anal. J. 3, 100041. https://doi.org/10.1016/j.dajour.2022.100041
[9] Escorcia-Gutierrez, J., Gamarra, M., Soto-Diaz, R., Pérez, M., Madera, N., Mansour, R.F., 2022. Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques. Agriculture 12, 977. https://doi.org/10.3390/agriculture12070977
[10] Gao, J., Zeng, W., Ren, Z., Ao, C., Lei, G., Gaiser, T., Srivastava, A.K., 2023. A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms. Agronomy 13, 1400. https://doi.org/10.3390/agronomy13051400
[11] Huy Cuong, N.H., Trinh, T.H., Nguyen, D.-H., Bui, T.K., Kiet, T.A., Ho, P.H., Thuy, N.T., 2022. An approach based on deep learning that recommends fertilizers and pesticides for agriculture recommendation. Int. J. Electr. Comput. Eng. IJECE 12, 5580. https://doi.org/10.11591/ijece.v12i5.pp5580-5588
[12] K, K.D., Kumar, J.P., 2024. Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System. J. Mach. Comput. 317–326. https://doi.org/10.53759/7669/jmc202404030
[13] Khan, A.A., Faheem, M., Bashir, R.N., Wechtaisong, C., Abbas, M.Z., 2022. Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation. IEEE Access 10, 129505–129519. https://doi.org/10.1109/ACCESS.2022.3228160
[14] León Chilito, E.D., Casanova Olaya, J.F., Corrales, J.C., Figueroa, C., 2025. Sustainability-driven fertilizer recommender system for coffee crops using case-based reasoning approach. Front. Sustain. Food Syst. 8, 1445795. https://doi.org/10.3389/fsufs.2024.1445795
[15] M, S., Vallabhaneni, R.S., Vasireddy, T., Polavarpu, D., 2022. Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models. Int. J. Interact. Mob. Technol. IJIM 16, 100–112. https://doi.org/10.3991/ijim.v16i16.31497
[16] Melasagare, S.M., Gawade, S., Narvekar, P., Pandit, S., 2024. Crop And Fertilizer RecommendationUsing Machine Learning 9.
[17] Ngo, V.M., Duong, T.-V.T., Nguyen, T.-B.-T., Dang, C.N., Conlan, O., 2023. A big data smart agricultural system: recommending optimum fertilisers for crops. Int. J. Inf. Technol. 15, 249–265. https://doi.org/10.1007/s41870-022-01150-1
[18] Patel*, K., B. Patel, H., 2023. Multi-criteria Agriculture Recommendation System using Machine Learning for Crop and Fertilizesrs Prediction. Curr. Agric. Res. J. 11, 137–149. https://doi.org/10.12944/CARJ.11.1.12
[19] Praynlin, E., n.d. Machine Learning Approach for Crop and Fertilizer Recommendation.
[20] Radočaj, D., Jurišić, M., Gašparović, M., 2022. The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture. Remote Sens. 14, 778. https://doi.org/10.3390/rs14030778
[21] Sajindra, H., Abekoon, T., Jayakody, J.A.D.C.A., Rathnayake, U., 2024. A novel deep learning model to predict the soil nutrient levels (N, P, and K) in cabbage cultivation. Smart Agric. Technol. 7, 100395. https://doi.org/10.1016/j.atech.2023.100395
[22] Sunandini, M., Deepiga, R., Gokulapriya, A., 2024. SMART SOIL FERTILIZER MONITORING AND CROP RECOMMENDATION SYSTEM BY USING IOT AND MACHINE LEARNING TECHNOLOGY 06.
[23] Tanaka, T.S.T., Heuvelink, G.B.M., Mieno, T., Bullock, D.S., 2024. Can machine learning models provide accurate fertilizer recommendations? Precis. Agric. 25, 1839–1856. https://doi.org/10.1007/s11119-024-10136-x
[24] H. Amnur, Y. Syanurdi, R. Idmayanti, and A. Erianda, “Developing Online Learning Applications for People with Hearing Impairment,” JOIV : Int. J. Inform. Visualization, vol. 5, no. 1, pp. 32–38, Mar. 2021, doi: 10.30630/joiv.5.1.457.
[25] Thorat, T., Patle, B.K., Kashyap, S.K., 2023. Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming. Smart Agric. Technol. 3, 100114. https://doi.org/10.1016/j.atech.2022.100114
[26] Tkatek, S., Amassmir, S., Belmzoukia, A., Abouchabaka, J., 2023. Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region. Int. J. Electr. Comput. Eng. IJECE 13, 5942. https://doi.org/10.11591/ijece.v13i5.pp5942-5950
[27] Vijender Reddy, G., Venkata Krishna Reddy, M., Spandana, K., Subbarayudu, Y., Albawi, A., Chandrashekar, R., Singla, A., Praveen, 2024. Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system. E3S Web Conf. 507, 01078. https://doi.org/10.1051/e3sconf/202450701078
[28] Zahra, S., Sharma, S., Kumar, S., 2025. The Agri-IQ Revolution: Crop and Fertilizer Recommendations Tailored by Nature. https://doi.org/10.20944/preprints202503.0376.v1