Precision agriculture is revolutionizing farming by leveraging
technology for efficient resource management and higher crop yields.
A significant challenge in this domain is accurate segmentation of
paddy fields from aerial and satellite imagery to monitor crop health
and optimize farming practices. This study introduces an Attention
Network (AttNet) framework designed specifically for paddy field
segmentation. AttNet incorporates attention mechanisms to enhance
spatial and contextual feature extraction, improving segmentation
accuracy over conventional models. The proposed method was trained
and tested on a dataset of satellite images, achieving a mean
Intersection over Union (mIoU) of 91.5% and pixel accuracy of 94.2%,
surpassing state-of-the-art methods such as U-Net (mIoU: 86.3%) and
DeepLabv3 (mIoU: 88.7%). This demonstrates its effectiveness in
handling complex paddy field patterns. The results validate AttNet as a
powerful tool for agricultural applications, promising to aid farmers
and policymakers in making informed decisions. Future research will
explore extending this framework to other crop types and integrating it
with real-time UAV systems.
K. Rajesh1, R. Krishnakumar2
Kalasalingam Academy of Research and Education, India1, Sri Ramakrishna Engineering College, India2
Precision Agriculture, Paddy Field Segmentation, Attention Networks, Deep Learning, Image Analysis
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 1 , Pages: 743 - 746 )
Date of Publication :
December 2024
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