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Analysis of crop prediction models using data analytics and ML techniques: a review

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Abstract

One of the essential fields contributing to a nation’s development is Agriculture. An efficient recommendation of crops is essential for strategic plan determination in the agricultural sector for promoting the farmer’s income and import–export policies. Crop prediction techniques are undertaken to suggest the types of crops to be grown in the specified field through the utilization of machine and deep learning approaches. Accurately predicting crops with minimized error has been highly challenging in recent trends. The proposed article provides a comprehensive review using machine and deep learning approaches to fulfil the increased importance of effective crop prediction. Initially, the recent scenario of crops is presented along with the short-term discussion over global need, worldwide demand and supply. Then the critical evaluation based on the existing reviews is made, and a comparative analysis is provided with a lack of review. Different machine and deep learning approaches are surveyed to analyze the performance variations and suggest suitable crops. Accordingly, the merits and demerits of various crop recommendation systems are analyzed. The analysis corresponds to crop varieties used for recommendation and diverse environmental factors considered in different datasets. Through this research, effective analysis of ML and DL methodologies in suitable crop recommendations can be analyzed. The different forms of crop varieties and environmental factors considered for better prediction of crops can be noticed. Also, the different forms of dataset used, challenges analyzed, and applications to be utilized are described. The reviews undertaken have represented an effective inclination towards learning models in predicting crops. The challenges can be identified through this survey, and it paves the way for developing an effective crop prediction model in future works.

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Authors and Affiliations

  1. Department of Technology DOT SPPU & MIT Academy of Engineering, SPPU Pune, Maharashtra, 411007, India

    Sachin Dattatraya Shingade

  2. Department of Instrumentation and Control Engineering, Government College of Engineering COEP, DOT SPPU Pune, Maharashtra, India

    Rohini Prashant Mudhalwadkar

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  1. Sachin Dattatraya Shingade
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  2. Rohini Prashant Mudhalwadkar
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Shingade, S.D., Mudhalwadkar, R.P. Analysis of crop prediction models using data analytics and ML techniques: a review. Multimed Tools Appl 83, 37813–37838 (2024). https://doi.org/10.1007/s11042-023-17038-6

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