Summary of Accurate Water Level Monitoring in Awd Rice Cultivation Using Convolutional Neural Networks, by Ahmed Rafi Hasan et al.
Accurate Water Level Monitoring in AWD Rice Cultivation Using Convolutional Neural Networks
by Ahmed Rafi Hasan, Niloy Kumar Kundu, Saad Hasan, Mohammad Rashedul Hoque, Swakkhar Shatabda
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to automate water height measurement in rice cultivation using computer vision is proposed. The Alternate Wetting and Drying (AWD) method is a sustainable alternative to Continuous Flooding (CF), but traditional manual measurements are time-consuming and prone to errors. Ultrasonic sensors offer improvements, but they have limitations such as susceptibility to weather conditions. A convolutional neural network (CNN) with an attention-based architecture is developed to accurately measure water height, achieving an R2 score of 0.9885 and a Mean Squared Error (MSE) of 0.2766. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to help rice farmers grow their crops more efficiently using special cameras and computers. Right now, farmers have to measure the water level in their fields by hand, which can be hard and might not be very accurate. The new system uses computer vision to take pictures of the water level and then uses those pictures to calculate the correct measurement. This makes it easier for farmers to use a method called Alternate Wetting and Drying (AWD) that is better for the environment. |
Keywords
» Artificial intelligence » Attention » Cnn » Mse » Neural network
