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

• URL: http://arxiv.org/abs/2412.08477v2
• Date: Thu, 12 Dec 2024 05:45:10 GMT
• Title: Accurate Water Level Monitoring in AWD Rice Cultivation Using Convolutional Neural Networks
• Authors: Ahmed Rafi Hasan, Niloy Kumar Kundu, Saad Hasan, Mohammad Rashedul Hoque, Swakkhar Shatabda,
• Abstract summary: Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce. 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. We propose a novel approach that automates water height measurement using computer vision, specifically through a convolutional neural network.
• Score: 1.2058600649065618
• License:
• Abstract: 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.

Related papers

• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• Federated Learning Approach to Mitigate Water Wastage [0.0]
  Residential outdoor water use in North America accounts for nearly 9 billion gallons daily, with approximately 50% of this wasted due to over-watering. Traditional approaches to reducing water wastage have focused on centralized data collection and processing. We propose a federated learning-based approach to optimize water usage in residential and agricultural settings.
  arXiv  Detail & Related papers  (2024-08-21T16:35:40Z)
• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field [4.091593765662773]
  Agricultural irrigation is a significant contributor to freshwater consumption. Current irrigation systems do not account for future soil moisture loss. This paper proposes a solution to improve irrigation efficiency, which is called DRLIC.
  arXiv  Detail & Related papers  (2023-04-04T01:04:53Z)
• A SWAT-based Reinforcement Learning Framework for Crop Management [0.0]
  We introduce a reinforcement learning (RL) environment that leverages the dynamics in the Soil and Water Assessment Tool (SWAT) This drastically saves time and resources that would have been otherwise deployed during a full-growing season. We demonstrate the utility of our framework by developing and benchmarking various decision-making agents following management strategies informed by standard farming practices and state-of-the-art RL algorithms.
  arXiv  Detail & Related papers  (2023-02-10T00:24:22Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery [0.7584685045934025]
  Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. We apply state-of-the-art self-supervised deep learning techniques to optical remote sensing data, and find that we are able to detect irrigation with up to nine times better precision, 90% better recall and 40% more generalization ability than the traditional supervised learning methods.
  arXiv  Detail & Related papers  (2021-08-12T01:13:04Z)
• High-resolution global irrigation prediction with Sentinel-2 30m data [0.8137198664755597]
  An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. We have developed a novel irrigation model and Python package ("Irrigation30") to generate 30m resolution irrigation predictions of cropland worldwide. Our model was able to achieve consistency scores in excess of 97% and an accuracy of 92% in a small geo-diverse randomly sampled test set.
  arXiv  Detail & Related papers  (2020-12-09T17:26:43Z)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.