Abstract
Fertilizer recommendations are essential for farmers: smallholders’ investment is relatively significant and risky with unknown yield responses and variable fertilizer prices. We utilized four learning models: MLP, SVR, GBRT, and LSTM, for fertilizer recommendation based on soil chemical analysis. This comparison work identifies the suitable prediction model for nutrients-based fertilizer recommendation that replaces agricultural expert advice. The real-time soil testing report data for three major crops like paddy, sugarcane, and banana are collected and applied for experimental purposes over four different prediction models. Different prediction error metrics are calculated and chosen the prediction model with the lowest error. It is clear from the comparative analysis that LSTM predicts better than others, with an accuracy of 99.15%.
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References
MA Yost NR Kitchen KA Sudduth EJ Sadler ST Drummond MR Volkmann 2017 Long-term impact of a precision agriculture system on grain crop production Precis Agric 18(5):823–842
MD McConnell 2019 Bridging the gap between conservation delivery and economics with precision agriculture Wildl Soc Bull 43(3):391–397. https://doi.org/10.1002/wsb.995
Swaminathan B, Palani S, Vairavasundaram S, Kotecha K, Kumar V (2023) IoT driven artificial intelligence technique for fertilizer recommendation model. IEEE Consum Electron Mag 12(2):109–117. https://doi.org/10.1109/MCE.2022.3151325
F Haghighat 2021 Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model Chaos Solitons Fractals 152:111399. https://doi.org/10.1016/J.CHAOS.2021.111399
TR Ayodele ASO Ogunjuyigbe A Amedu JL Munda 2019 Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms Renew Energy Focus 29:78–93
JH Friedman 2001 Greedy function approximation: a gradient boosting machine Ann Stat 29(5):1189–1232
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Acknowledgements
The authors gratefully acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology, India. The authors are grateful to the Indian Council of Social Science Research (ICSSR), New Delhi, for the financial support (No. IMPRESS/P580/278/2018-19/ICSSR) under IMPRESS Scheme. The authors express their gratitude to SASTRA Deemed University, Thanjavur, for providing the infrastructure to carry out this research work.
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Swaminathan, B., Saravanan, P., Subramaniyaswamy, V. (2023). Fertilizer Recommendation System for High Crop Yield based on Prediction Model: A Comparative Analysis. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_1
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DOI: https://doi.org/10.1007/978-981-99-3656-4_1
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