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E-CropReco: a dew-edge-based multi-parametric crop recommendation framework for internet of agricultural things

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Abstract

Crop productivity prediction and recommendation is a significant research area of smart agriculture. This paper proposes an Internet of Things (IoT) framework based on dew computing, edge computing, and federated learning, where soil parameters, environmental parameters, and weather data are analysed to predict the crop productivity of a land, and then recommend suitable crop for the land. The dew layer pre-processes and accumulates the received sensor data, and forwards to the edge server. The edge server analyses the sensor data and the weather data, and then sends the result to the cloud along with the model characteristics and to the mobile device. The proposed framework is simulated in iFogSim. The theoretical analysis shows that the proposed framework has reduced the delay by 60–70% approximately and power consumption by 70–80% approximately than the conventional sensor-cloud framework. We also observe that the proposed framework has reduced the delay by 12–35% approximately and power consumption by 30–50% approximately than the edge-cloud framework. We compare four machine learning algorithms based on their performance in data analysis in terms of precision, recall, accuracy, and F-score. We observe that each classifier obtains more than 95% prediction accuracy. An Android application is also proposed for crop recommendation.

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Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on request.

Notes

  1. https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset.

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Authors and Affiliations

  1. Department of Computer Science, Mahishadal Raj College, Mahishadal, West Bengal, India

    Somnath Bera, Tanushree Dey & Anwesha Mukherjee

  2. Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Rajkumar Buyya

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  1. Somnath Bera
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  2. Tanushree Dey
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  3. Anwesha Mukherjee
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  4. Rajkumar Buyya
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Contributions

SB contributed to conceptualization, formal analysis, methodology, and writing—original draft. TD contributed to conceptualization, methodology, data analysis, and writing—original draft. AM contributed to conceptualization, methodology, supervision, and writing—review and editing. RB contributed to supervision and writing— review and editing.

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Correspondence to Anwesha Mukherjee.

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Bera, S., Dey, T., Mukherjee, A. et al. E-CropReco: a dew-edge-based multi-parametric crop recommendation framework for internet of agricultural things. J Supercomput 79, 11965–11999 (2023). https://doi.org/10.1007/s11227-023-05131-4

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  • DOI: https://doi.org/10.1007/s11227-023-05131-4

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