Automated Farm Ponds Detection using Satellite Imagery and Deep Learning: Insights from Kadwanchi village, Maharashtra
DOI:
https://doi.org/10.58825/jog.2025.19.1.220Keywords:
Deep Learning, , remote sensing, Satellite data, ArcGIS, Geographic Information System (GIS)Abstract
Farm ponds play a crucial role in rainwater harvesting and irrigation, making their accurate detection essential for effective water resource management. This study explores the application of deep learning models for detecting farm ponds in Kadwanchi village, Maharashtra, where water scarcity challenges agricultural productivity. Using satellite imagery, the study compares and evaluates four deep learning models—U-Net, Mask RCNN, DeepLabV3+, and Feature Classifier—based on precision, recall, and F1 score. The Feature Classifier emerged as the most accurate model, achieving a perfect precision score of 1.0, a recall of 0.863, and an F1 score of 0.927, detecting 296 farm ponds. U-Net also performed well, with an F1 score of 0.873, while Mask RCNN and DeepLabV3+ showed more moderate results. These findings can assist government agencies in making data-driven decisions about water resource management and promoting sustainable agriculture in water-scarce regions. Future research could focus on hybrid models and larger datasets to improve farm pond detection accuracy.
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