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Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

P. S. S. Gopi*, M. Karthikeyan

Department of Computer and Information Science, Faculty of Science, Annamalai University, Chidambaram, 608002, India

* Corresponding Author: P. S. S. Gopi. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 313-326. https://doi.org/10.32604/iasc.2023.029756

Abstract

Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction. At the initial stage, equilibrium optimizer (EO) with kernel extreme learning machine (KELM) technique is employed for effectual recommendation of crops. Next, random forest (RF) technique was executed for predicting the crop yield accurately. For reporting the improved performance of the MMML-CRYP system, a wide range of simulations were carried out and the results are investigated using benchmark dataset. Experimentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

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Cite This Article

P. S. S. Gopi and M. Karthikeyan, "Multimodal machine learning based crop recommendation and yield prediction model," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 313–326, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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