Semantic Segmentation of Paddy Parcels Using Deep Neural Networks Based on DeepLabV3

Syazwani Basir, Nurul Aziz, Nurshafiza Abiddin

2024

Abstract

Paddy parcels are frequently converted to other structures which contributes significantly to changes in paddy cultivation areas and a decrease in rice production. Determining the current land use status for paddy parcels annually is quite challenging; thus, the Paddy Geospatial Information System (MakGeoPadi) has been developed to determine the precise Malaysian paddy cultivation regions in order to provide a sufficient food supply for the entire country. Deep convolutional neural network (DCNN) algorithms such as DeepLabV3 are used in this study to accurately estimate paddy yield of 12 granaries. The objective of this study is to enhance the DeepLabV3 paddy parcel detection model to generate data that can be relied upon for reliable decision-making. Deep-learning applications based on the DeepLabV3 model were classified into four classes: active paddy parcel (PA), miscellaneous paddy parcel (PP), permanent structures (SK) and permanent crop (TK) using ResNet50 in ArcGIS Pro version 2.9. DCNN has been utilised to perform semantic segmentation. The DCNN architecture known as DeepLabV3 is trained using the 16,000 datasets in the experiment, with Pleiades satellite images scaled at 224 x 224-pixel sizes. Following the training phase, the DeepLabV3 model achieved the highest successful training accuracy, scoring 91.6%.

Paper Citation

in Harvard Style

Basir S., Aziz N. and Abiddin N. (2024). Semantic Segmentation of Paddy Parcels Using Deep Neural Networks Based on DeepLabV3. In Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-694-1, SciTePress, pages 173-180. DOI: 10.5220/0012698200003696

in Bibtex Style

@conference{gistam24,
author={Syazwani Basir and Nurul Aziz and Nurshafiza Abiddin},
title={Semantic Segmentation of Paddy Parcels Using Deep Neural Networks Based on DeepLabV3},
booktitle={Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2024},
pages={173-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012698200003696},
isbn={978-989-758-694-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Semantic Segmentation of Paddy Parcels Using Deep Neural Networks Based on DeepLabV3
SN - 978-989-758-694-1
AU - Basir S.
AU - Aziz N.
AU - Abiddin N.
PY - 2024
SP - 173
EP - 180
DO - 10.5220/0012698200003696
PB - SciTePress