用深度学习自动监测水稻田水位，提升精准灌溉效率。
Accurate Water Level Monitoring in AWD Rice Cultivation Using Convolutional Neural Networks
- 基于注意力机制的卷积神经网络，通过图像识别水位高度。
- 模型准确率R²达0.9885，误差仅0.2766，优于传统传感器。
- 适合智能农业、智慧灌溉系统研发人员参考。
交替湿干（AWD）是一种被推广为可持续水稻种植节水管理方法，以替代持续淹水（CF）。气候变化使农业面临挑战，全球水资源日益紧缺，影响灌溉低地稻作生产。水稻作为全球超过一半人口的主食，用水量远高于其他主要作物。在孟加拉国，博罗稻在生长期间需大量用水。传统上农民依靠人工测量水位，耗时且易出错。虽然超声波传感器提升了测量精度，但仍受天气和环境因素影响。为此，本文提出一种基于计算机视觉的自动化水位测量新方法，采用卷积神经网络（CNN）实现。所提出的注意力机制架构在测试中达到R² = 0.9885，均方误差（MSE）为0.2766，显著提升了对AWD系统的水位监控准确性与效率。
原文摘要 · Abstract (English)
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.
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