Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

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%.

Precision Agriculture Advisor

“…Machine learning classi ers such as decision trees, support vector machines, random forests, and neural networks have been widely employed in crop prediction tasks, demonstrating superior performance in handling complex and high-dimensional data. By synthesizing insights from these studies, this paper aims to contribute to the eld by proposing a comprehensive framework for crop prediction that leverages various feature selection techniques and classi ers to maximize predictive accuracy and robustness in diverse agricultural environments [9].…”