Accurate Water Level Monitoring in AWD Rice Cultivation Using Convolutional Neural Networks

The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Ahmed Rafi Hasan, Kundu, Niloy Kumar, Saad Hasan, Hoque, Mohammad Rashedul, Shatabda, Swakkhar
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
Cultivation
Lowlands
Neural networks
Rice
Water levels
Water management
Water resources
Weather
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Summary:The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce, affecting rice production on irrigated lowlands. Rice, a staple food for over half of the world's population, demands significantly more water than other major crops. In Bangladesh, Boro rice, in particular, requires considerable water inputs during its cultivation. Traditionally, farmers manually measure water levels, a process that is both time-consuming and prone to errors. While ultrasonic sensors offer improvements in water height measurement, they still face limitations, such as susceptibility to weather conditions and environmental factors. To address these issues, we propose a novel approach that automates water height measurement using computer vision, specifically through a convolutional neural network (CNN). Our attention-based architecture achieved an \(R^2\) score of 0.9885 and a Mean Squared Error (MSE) of 0.2766, providing a more accurate and efficient solution for managing AWD systems.
ISSN:2331-8422