Abstract
The economy of Bangladesh depends to a large extent on agriculture. Besides, a large number of the total population are employed in this sector. In Bangladesh, the population is fast expanding while the overall amount of arable land is constantly diminishing. Because various crops require different soil types, identifying and selecting the proper kind of soil is critical to ensuring optimal crop yield while working with limited land resources. In this study, we present a soil classification method using symbolic regression of multigene genetic programming. Dataset for this work is collected from Soil Resource Development Institute, Government of the people’s republic of Bangladesh. GPTIPS toolbox is used to select the appropriate features for training and developing a mathematical model. In a short period of time, the model generates correct results for both the training and testing datasets. Besides, the error rate for soil type classification is extremely low. Finally, suitable crops are recommended based on the accurate classification. According to the results, the proposed multigene genetic programming (MGGP)-based approach performs the best in terms of accuracy, with an accuracy of 98.04 percent. Moreover, our proposed soil classification method outperforms many existing soil classification methods.
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Khan, I., Shill, P.C. (2023). A Multigene Genetic Programming Approach for Soil Classification and Crop Recommendation. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_32
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DOI: https://doi.org/10.1007/978-981-19-7528-8_32
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