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Simulating crop yield estimation and prediction through geospatial data for specific regional analysis

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Abstract

Geospatial imagery play a key role in deciding land usage for agrarian planning and assessment by acknowledging the food security problems, impacts of climatic changes, and global population increase. The proposed approach provides geospatial methods combined with machine learning methods to predict crop yield for a specific Region of Interest, including different weather patterns. This research utilizes U-Net and Random Forest algorithm to predict the agricultural yield estimation and comprehensively analyse the yield prediction specific to the Vellore region of interest. The study area of 28.4 hectares is validated with specific labelled classes to estimate the agricultural produce. The proposed method demonstrates a well-suited yield mapping of vegetation from sentinel images through a combination of U-Net and RF at 99.38% for large-scale crop yield prediction.

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Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sandeep Kumar Mathivanan & Prabhu Jayagopal

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  1. Sandeep Kumar Mathivanan
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  2. Prabhu Jayagopal
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Correspondence to Prabhu Jayagopal.

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Communicated by: H. Babaie

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Mathivanan, S.K., Jayagopal, P. Simulating crop yield estimation and prediction through geospatial data for specific regional analysis. Earth Sci Inform 16, 1005–1023 (2023). https://doi.org/10.1007/s12145-022-00887-4

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