Abstract
We present an improved GA-based approach for nutrient recommendation that utilizes time-series sensor data and suggests various settings for different crops in this article. After that, a neighborhood-based method is provided for handling exploration and exploitation in order to optimize the parameters to maximize the production. The final judgment is determined based on the similarity between recommendations of the patterns and real-time sensor data. The experimental findings indicate that the suggested model is capable of recommending optimal patterns and assisting in effectively increasing the annual production.
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Ahmed, U., Lin, J.CW., Srivastava, G., Wu, J.MT. (2022). An Improved GA-Based Recommendation System for Soil Fertilization. In: Chu, SC., Lin, J.CW., Li, J., Pan, JS. (eds) Genetic and Evolutionary Computing. ICGEC 2021. Lecture Notes in Electrical Engineering, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-16-8430-2_56
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DOI: https://doi.org/10.1007/978-981-16-8430-2_56
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