Farmland Segmentation for Autonomous Agricultural Machinery 


Vol. 50,  No. 4, pp. 587-594, Apr.  2025
10.7840/kics.2025.50.4.587


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  Abstract

Smart agriculture leverages information and communication technology in farming to enable automation, providing a sustainable solution to challenges such as climate change and an aging population. Recently, there has been active research on agricultural automation by integrating autonomous driving technology into key agricultural equipment, such as tractors and rice planters. This paper proposes a deep learning architecture to distinguish cultivable land. Using images of farmland captured by drones, we construct a dataset and aim to classify areas such as fields, edges, and roads with a lightweight deep learning model. This paper proposes a deep learning model that refines image regions using a DG-block (Dilated Group Convolution-block) and pixel shuffle. The proposed system demonstrates performance with an mIOU of 78.4%, an accuracy of 77.7%, and an inference time of 50ms.

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[IEEE Style]

N. Y. Bae, S. K. Choi, D. S. Han, "Farmland Segmentation for Autonomous Agricultural Machinery," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 587-594, 2025. DOI: 10.7840/kics.2025.50.4.587.

[ACM Style]

Na Yeon Bae, Sung Kyun Choi, and Dong Seog Han. 2025. Farmland Segmentation for Autonomous Agricultural Machinery. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 587-594. DOI: 10.7840/kics.2025.50.4.587.

[KICS Style]

Na Yeon Bae, Sung Kyun Choi, Dong Seog Han, "Farmland Segmentation for Autonomous Agricultural Machinery," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 587-594, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.587)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880