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Crop mapping through hybrid capsule transient auto-encoder technique based on radar features

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Abstract

Agriculture is considered as the important field which makes its huge contribution over the country’s economic growth. The yield of food crops and the precise categorization of crops based on several characteristics are of primary importance in this agricultural industry. However, due to a lack of an effective classification method, this industry has significant issues correctly classifying the crops. In addition, classifying food crops using data mining is highly efficient as these techniques can deal with huge amounts of crop data. To this extent, this paper proposes an efficient classification model based on the cropland data extracted from the cropland images. Initially, the dataset is pre-processed based on data-mining techniques like data cleaning and data discretization. Then, the data are clustered based on their relevance using an Improved Density-based Spatial Clustering of Applications with Noise (IDBSCAN) clustering technique. Finally, classification is performed accurately using the Adaptive Capsule Transient Auto-Encoder (ACTAE). The experimental validation of a proposed approach proved its efficiency over the other existing models with an overall accuracy rate of 97% which is incomparable to the other crop classification models implemented over the cropland dataset.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India

    Kranthi Madala & M. Siva Ganga Prasad

  2. Department of Electronics and Instrumentation Engineering, V.R. Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India

    Kranthi Madala

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  1. Kranthi Madala
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  2. M. Siva Ganga Prasad
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Correspondence to Kranthi Madala.

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Madala, K., Prasad, M.S.G. Crop mapping through hybrid capsule transient auto-encoder technique based on radar features. Multimed Tools Appl 83, 43727–43757 (2024). https://doi.org/10.1007/s11042-023-17327-0

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