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Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm

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Abstract

A proper soil prediction is one of the most important parameters to decide the suitable crop which is generally performed manually by the farmers. Therefore, the efficiency of the farmers may be increased by producing an automated tools for soil prediction. This paper presents an automated system for categorization of the soil datasets into respective categories using images of the soils which can further be used for the decision of crops. For the same, a novel Bag-of-words and chaotic spider monkey optimization based method has been proposed which is used to classify the soil images into its respective categories. The novel chaotic spider monkey optimization algorithm shows desirable convergence and improved global search ability over standard benchmark functions. Hence, it has been used to cluster the keypoints in Bag-of-words method for soil prediction. The experimental outcomes illustrate that the anticipated methods effectively classify the soil in comparison to other meta-heuristic based methods.

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Authors and Affiliations

  1. Amity University Rajasthan, Jaipur, India

    Sandeep Kumar & Ramesh C. Poonia

  2. Jagannath University, Jaipur, Rajasthan, India

    Basudev Sharma & Vivek Kumar Sharma

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  1. Sandeep Kumar
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  2. Basudev Sharma
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  3. Vivek Kumar Sharma
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  4. Ramesh C. Poonia
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Correspondence to Ramesh C. Poonia.

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Kumar, S., Sharma, B., Sharma, V.K. et al. Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evol. Intel. 14, 293–304 (2021). https://doi.org/10.1007/s12065-018-0186-9

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