By Shivamani G & Surya | Published on January 15, 2024
This innovative research addresses the critical challenge of optimizing irrigation canal design, particularly in regions facing water scarcity like Telangana and Andhra Pradesh. Traditional methods often result in inefficient layouts, leading to water wastage and unequal distribution — issues that directly affect agricultural productivity and rural livelihoods. To solve this, the researchers developed AI-powered systems named Hydro-Net and its enhanced version Hydro-Net+, which leverage satellite imagery and advanced machine learning to identify optimal canal routes.
Hydro-Net begins by analyzing satellite images (e.g., from Sentinel-2) using segmentation models to detect existing water bodies. Hydro-Net+ advances this by also classifying different land types, helping the system understand which terrains are easier or harder to build canals through. Once this information is gathered, the systems use an intelligent routing algorithm called Near-Optimal Bidirectional Search (NBS) to determine the best canal path. This algorithm is more efficient and accurate than traditional ones like A* or Dijkstra’s. By integrating terrain awareness with efficient pathfinding, Hydro-Net+ makes it possible to design canals that are both cost-effective and sustainable, significantly improving water access in agriculture-dependent regions.
12
5