STACKING ENSEMBLE-BASED PREDICTIVE SYSTEM FOR CROP RECOMMENDATION
DOI:
https://doi.org/10.33003/fjs-2024-0806-2856Keywords:
Ensemble Learning, Crop Selection, Precision Agriculture, Predictive Modeling, Proof-of-Concept SystemAbstract
Agricultural sustainability relies on crop production, but the task of choosing appropriate crops for certain places is difficult owing to the ever-changing environmental circumstances. Traditional approaches are often limited in scope, failing to adapt to diverse soil types and environmental parameters. This study introduces a novel prediction method that utilizes a machine-learning model with ensemble approaches to provide recommendations for crops. The system was developed using a Design Science Research (DSR) methodology. The proposed model incorporates a wide array of machine-learning techniques, including K-Nearest Neighbors, Decision Trees, Support Vector Machines, Naive Bayes, Logistic Regression, and Extreme Gradient Boosting. The integration utilizes the Random Forest meta-model. The model was trained and validated using a large dataset gathered from Kaggle, which consisted of a wide variety of crops and environmental characteristics. The model's performance was evaluated using metrics such as Accuracy, Recall, F1-Score, and Precision. It exhibited outstanding accuracy of 99.8%, along with superior recall, precision, and F1 scores, outperforming previous research by a significant margin. Furthermore, data flow diagrams illustrate the data processing flow within the system. The implementation was carried out using the Python programming language, with MongoDB employed for database development. The resulting proof-of-concept system demonstrates the practical applicability of the model by providing reliable crop recommendations based on environmental data. This research marks a substantial advancement in optimizing crop management strategies through advanced predictive modeling, offering a robust tool to aid farmers in making informed decisions, ultimately enhancing agricultural productivity and sustainability.
References
Akkem, Y., Biswas, S. K., & Varanasi, A. (2024). A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Engineering Applications of Artificial Intelligence, 131, 107881.
Ali, Z. A., Abduljabbar, Z. H., Taher, H. A., Sallow, A. B., & Almufti, S. M. (2023). Exploring the power of eXtreme gradient boosting algorithm in machine learning: A review. Academic Journal of Nawroz University, 12(2), 320-334.
Alnuaimi, A. F., & Albaldawi, T. H. (2024). An overview of machine learning classification techniques. In BIO Web of Conferences (Vol. 97, p. 00133). EDP Sciences.
Alnuaimi, A. F., & Albaldawi, T. H. (2024). An overview of machine learning classification techniques. In BIO Web of Conferences (Vol. 97, p. 00133). EDP Sciences.
Amanullah, A., & Khan, U. (2024). Advancing sustainable agriculture with beneficial microbes: Enhancing crop growth and yield for food security and human health. Advances in Modern Agriculture, 4(2).
Balaska, V., Adamidou, Z., Vryzas, Z., & Gasteratos, A. (2023). Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 11(8), 774.
Bian, Z., Vong, C. M., Wong, P. K., & Wang, S. (2020). Fuzzy KNN method with adaptive nearest neighbors. IEEE transactions on cybernetics, 52(6), 5380-5393.
Chen, J., Zhao, F., Sun, Y., & Yin, Y. (2020). Improved XGBoost model based on genetic algorithm. International Journal of Computer Applications in Technology, 62(3), 240-245.
Cheng, F., Yang, C., Zhou, C., Lan, L., Zhu, H., & Li, Y. (2020). Simultaneous determination of metal ions in zinc sulfate solution using UVVis spectrometry and SPSE-XGBoost method. Sensors, 20(17), 4936.
Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1), 123.
Costa, V. G., & Pedreira, C. E. (2023). Recent advances in decision trees: An updated survey. Artificial Intelligence Review, 56(5), 4765-4800.
COSTEA, M., LEAH, D., PACALU, R., MULEAC, L., STANCIU, S., MERGHE, P., ... & FEHER, A. (2023). CHALLENGES AND ISSUES FOR FARMERS IN THE MODERN ERA. Research Journal of Agricultural Science, 55(2).
Cunningham, P., & Delany, S. J. (2020). k-Nearest neighbour classifiers: (with Python examples). arXiv preprint arXiv:2004.04523.
Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 54(6), 1-25.
Fong, K. S., & Motani, M. (2024, March). Symbolic Regression Enhanced Decision Trees for Classification Tasks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 11, pp. 12033-12042).
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.
Glanois, C., Weng, P., Zimmer, M., Li, D., Yang, T., Hao, J., & Liu, W. (2024). A survey on interpretable reinforcement learning. Machine Learning, 1-44.
Hassan, M., Rai, P., & Maharjan, S. (2023). Empowering south asian agricultural communities: A comprehensive approach to iot-driven agriculture through awareness, training, and collaboration. Quarterly Journal of Emerging Technologies and Innovations, 8(3), 18-32.
Huber, F., Yushchenko, A., Stratmann, B., & Steinhage, V. (2022). Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches. Computers and Electronics in Agriculture, 202, 107346.
Isnaeni, I. A., Indriani, S., Zaman, M. R. N., & Nugroho, A. (2024). Comparison of K-Nearest Neighbors (KNN) and Decision Tree with Binary Particle Swarm Optimization (BPSO) in Predicting Employee Performance. International Journal of Open Information Technologies, 12(3), 57-65.
Ketheneni, K., Yenuga, P., Garnepudi, P., Paleti, L., Burla, N. R., Srinivas, O., ... & Yamarthi, N. R. (2024). Crop, Fertilizer and Pesticide Recommendation using Ensemble Method and Sequential Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 473-485.
Khatri, P., Kumar, P., Shakya, K. S., Kirlas, M. C., & Tiwari, K. K. (2023). Understanding the intertwined nature of rising multiple risks in modern agriculture and food system. Environment, Development and Sustainability, 1-44.
Koshariya, A. K., Rameshkumar, P. M., Balaji, P., Cavaliere, L. P. L., Dornadula, V. H. R., & Singh, B. (2024). Data-Driven Insights for Agricultural Management: Leveraging Industry 4.0 Technologies for Improved Crop Yields and Resource Optimization. In Robotics and Automation in Industry 4.0 (pp. 260-274). CRC Press.
Kumar, M., Bajaj, K., Sharma, B., & Narang, S. (2022). A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models. Big Data, 10(5), 371-387.
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1), 18.
Maheswary, A., Nagendram, S., Kiran, K. U., Ahammad, S. H., Priya, P. P., Hossain, M. A., & Rashed, A. N. Z. (2024). Intelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy. Journal of The Institution of Engineers (India): Series B, 1-9.
Mbanaso, U. M., Abrahams, L., & Okafor, K. C. (2023). Research Techniques for Computer Science, Information Systems and Cybersecurity (1st ed.). Springer.
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University-Computer and Information Sciences, 35(2), 757-774.
Nanfack, G., Temple, P., & Frnay, B. (2022). Constraint enforcement on decision trees: A survey. ACM Computing Surveys (CSUR), 54(10s), 1-36.
Oates, B. J. (2006). Researching Information Systems and Computing (First, Vol. 1). SAGE Publications.
Phadke, M., Goel, M., Bajpai, R., & Mehta, N. (2022). Designing an algorithm to support optimized crop selection by farmers. In ICT Analysis and Applications (pp. 345-357). Springer Singapore.
Raju, C., Ashoka, D. V., & BV, A. P. (2024). CropCast: Harvesting the future with interfused machine learning and advanced stacking ensemble for precise crop prediction. Kuwait Journal of Science, 51(1), 100160.
Reddy, G. V., Reddy, M. V. K., Spandana, K., Subbarayudu, Y., Albawi, A., Chandrashekar, R., ... & Praveen, P. (2024). Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system. In E3S Web of Conferences (Vol. 507, p. 01078). EDP Sciences.
Rimanic, L., Renggli, C., Li, B., & Zhang, C. (2020). On convergence of nearest neighbor classifiers over feature transformations. Advances in Neural Information Processing Systems, 33, 12521-12532.
Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
Satish, N., Anmala, J., Rajitha, K., & Varma, M. R. (2024). A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India. Ecological Informatics, 80, 102500.
Sunil Kumar, S. (2024). MACHINE LEARNING-BASED CROP RECOMMENDATION SYSTEM FOR ENHANCED YIELD PRODUCTION. Journal of Interdisciplinary Cycle Research, 26(1), 597-611.
Thakur, A., Kumari, D., & Kumar, P. (2024). Climate Change and Agriculture: Assessing the relationship between agricultural practices, greenhouse gas emissions, and climate change. A Comprehensive Exploration of Soil, Water, and Air Pollution in Agriculture, 155.
Verma, G., & Sahu, T. P. (2024). A correlation-based feature weighting filter for multi-label Naive Bayes. International Journal of Information Technology, 16(1), 611-619.
Vezirolu, M., Ezirolu, E., & Bucak, . . (2024). Performance Comparison between Naive Bayes and Machine Learning Algorithms for News Classification. In Bayesian Inference-Recent Trends. IntechOpen.
Wade, C., & Glynn, K. (2020). Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python. Packt Publishing Ltd.
Yang, Y., Lv, H., & Chen, N. (2023). A survey on ensemble learning under the era of deep learning. Artificial Intelligence Review, 56(6), 5545-5589.
Zhu, L., Wang, L., Yang, Z., Xu, P., & Yang, S. (2024). PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information. Interdisciplinary Sciences: Computational Life Sciences, 1-26.
Ali, Z. A., Abduljabbar, Z. H., Taher, H. A., Sallow, A. B., & Almufti, S. M. (2023). Exploring the power of eXtreme gradient boosting algorithm in machine learning: A review. Academic Journal of Nawroz University, 12(2), 320-334.
Alnuaimi, A. F., & Albaldawi, T. H. (2024). An overview of machine learning classification techniques. In BIO Web of Conferences (Vol. 97, p. 00133). EDP Sciences.
Alnuaimi, A. F., & Albaldawi, T. H. (2024). An overview of machine learning classification techniques. In BIO Web of Conferences (Vol. 97, p. 00133). EDP Sciences.
Amanullah, A., & Khan, U. (2024). Advancing sustainable agriculture with beneficial microbes: Enhancing crop growth and yield for food security and human health. Advances in Modern Agriculture, 4(2).
Balaska, V., Adamidou, Z., Vryzas, Z., & Gasteratos, A. (2023). Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 11(8), 774.
Bian, Z., Vong, C. M., Wong, P. K., & Wang, S. (2020). Fuzzy KNN method with adaptive nearest neighbors. IEEE transactions on cybernetics, 52(6), 5380-5393.
Chen, J., Zhao, F., Sun, Y., & Yin, Y. (2020). Improved XGBoost model based on genetic algorithm. International Journal of Computer Applications in Technology, 62(3), 240-245.
Cheng, F., Yang, C., Zhou, C., Lan, L., Zhu, H., & Li, Y. (2020). Simultaneous determination of metal ions in zinc sulfate solution using UVVis spectrometry and SPSE-XGBoost method. Sensors, 20(17), 4936.
Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1), 123.
Costa, V. G., & Pedreira, C. E. (2023). Recent advances in decision trees: An updated survey. Artificial Intelligence Review, 56(5), 4765-4800.
COSTEA, M., LEAH, D., PACALU, R., MULEAC, L., STANCIU, S., MERGHE, P., ... & FEHER, A. (2023). CHALLENGES AND ISSUES FOR FARMERS IN THE MODERN ERA. Research Journal of Agricultural Science, 55(2).
Cunningham, P., & Delany, S. J. (2020). k-Nearest neighbour classifiers: (with Python examples). arXiv preprint arXiv:2004.04523.
Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 54(6), 1-25.
Fong, K. S., & Motani, M. (2024, March). Symbolic Regression Enhanced Decision Trees for Classification Tasks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 11, pp. 12033-12042).
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.
Glanois, C., Weng, P., Zimmer, M., Li, D., Yang, T., Hao, J., & Liu, W. (2024). A survey on interpretable reinforcement learning. Machine Learning, 1-44.
Hassan, M., Rai, P., & Maharjan, S. (2023). Empowering south asian agricultural communities: A comprehensive approach to iot-driven agriculture through awareness, training, and collaboration. Quarterly Journal of Emerging Technologies and Innovations, 8(3), 18-32.
Huber, F., Yushchenko, A., Stratmann, B., & Steinhage, V. (2022). Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches. Computers and Electronics in Agriculture, 202, 107346.
Isnaeni, I. A., Indriani, S., Zaman, M. R. N., & Nugroho, A. (2024). Comparison of K-Nearest Neighbors (KNN) and Decision Tree with Binary Particle Swarm Optimization (BPSO) in Predicting Employee Performance. International Journal of Open Information Technologies, 12(3), 57-65.
Ketheneni, K., Yenuga, P., Garnepudi, P., Paleti, L., Burla, N. R., Srinivas, O., ... & Yamarthi, N. R. (2024). Crop, Fertilizer and Pesticide Recommendation using Ensemble Method and Sequential Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 473-485.
Khatri, P., Kumar, P., Shakya, K. S., Kirlas, M. C., & Tiwari, K. K. (2023). Understanding the intertwined nature of rising multiple risks in modern agriculture and food system. Environment, Development and Sustainability, 1-44.
Koshariya, A. K., Rameshkumar, P. M., Balaji, P., Cavaliere, L. P. L., Dornadula, V. H. R., & Singh, B. (2024). Data-Driven Insights for Agricultural Management: Leveraging Industry 4.0 Technologies for Improved Crop Yields and Resource Optimization. In Robotics and Automation in Industry 4.0 (pp. 260-274). CRC Press.
Kumar, M., Bajaj, K., Sharma, B., & Narang, S. (2022). A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models. Big Data, 10(5), 371-387.
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1), 18.
Maheswary, A., Nagendram, S., Kiran, K. U., Ahammad, S. H., Priya, P. P., Hossain, M. A., & Rashed, A. N. Z. (2024). Intelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy. Journal of The Institution of Engineers (India): Series B, 1-9.
Mbanaso, U. M., Abrahams, L., & Okafor, K. C. (2023). Research Techniques for Computer Science, Information Systems and Cybersecurity (1st ed.). Springer.
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University-Computer and Information Sciences, 35(2), 757-774.
Nanfack, G., Temple, P., & Frnay, B. (2022). Constraint enforcement on decision trees: A survey. ACM Computing Surveys (CSUR), 54(10s), 1-36.
Oates, B. J. (2006). Researching Information Systems and Computing (First, Vol. 1). SAGE Publications.
Phadke, M., Goel, M., Bajpai, R., & Mehta, N. (2022). Designing an algorithm to support optimized crop selection by farmers. In ICT Analysis and Applications (pp. 345-357). Springer Singapore.
Raju, C., Ashoka, D. V., & BV, A. P. (2024). CropCast: Harvesting the future with interfused machine learning and advanced stacking ensemble for precise crop prediction. Kuwait Journal of Science, 51(1), 100160.
Reddy, G. V., Reddy, M. V. K., Spandana, K., Subbarayudu, Y., Albawi, A., Chandrashekar, R., ... & Praveen, P. (2024). Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system. In E3S Web of Conferences (Vol. 507, p. 01078). EDP Sciences.
Rimanic, L., Renggli, C., Li, B., & Zhang, C. (2020). On convergence of nearest neighbor classifiers over feature transformations. Advances in Neural Information Processing Systems, 33, 12521-12532.
Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
Satish, N., Anmala, J., Rajitha, K., & Varma, M. R. (2024). A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India. Ecological Informatics, 80, 102500.
Sunil Kumar, S. (2024). MACHINE LEARNING-BASED CROP RECOMMENDATION SYSTEM FOR ENHANCED YIELD PRODUCTION. Journal of Interdisciplinary Cycle Research, 26(1), 597-611.
Thakur, A., Kumari, D., & Kumar, P. (2024). Climate Change and Agriculture: Assessing the relationship between agricultural practices, greenhouse gas emissions, and climate change. A Comprehensive Exploration of Soil, Water, and Air Pollution in Agriculture, 155.
Verma, G., & Sahu, T. P. (2024). A correlation-based feature weighting filter for multi-label Naive Bayes. International Journal of Information Technology, 16(1), 611-619.
Vezirolu, M., Ezirolu, E., & Bucak, . . (2024). Performance Comparison between Naive Bayes and Machine Learning Algorithms for News Classification. In Bayesian Inference-Recent Trends. IntechOpen.
Wade, C., & Glynn, K. (2020). Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python. Packt Publishing Ltd.
Yang, Y., Lv, H., & Chen, N. (2023). A survey on ensemble learning under the era of deep learning. Artificial Intelligence Review, 56(6), 5545-5589.
Zhu, L., Wang, L., Yang, Z., Xu, P., & Yang, S. (2024). PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information. Interdisciplinary Sciences: Computational Life Sciences, 1-26.
Published
2024-12-31
How to Cite
Abdullahi, M. U., Aimufua, G. I. O., Morufu , O., & Usman , U. F. (2024). STACKING ENSEMBLE-BASED PREDICTIVE SYSTEM FOR CROP RECOMMENDATION. FUDMA JOURNAL OF SCIENCES, 8(6), 72 - 83. https://doi.org/10.33003/fjs-2024-0806-2856
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Section
Research Articles
FUDMA Journal of Sciences
