Utilizing a fusion of remote sensing data and machine learning models to forecast flood risks to agriculture in Hanoi City, Vietnam

Artificial intelligence (AI) technology, such as computer vision methods and Deep learning algorithms, have become potent instruments for transforming agricultural practices. Artificial Intelligence (AI) facilitates real-time monitoring of crop growth, health, & yield prediction by evaluating data from multiple sources, including weather sensors, satellite imaging, and IoT devices. The potential of AI-based systems to improve crop management techniques through precision agriculture—which involves focused pest control, irrigation, & fertilizer application—is highlighted in this abstract. To fully realize the advantages of AI in crop monitoring, however, issues like data privacy & model interpretability need to be resolved. In general, the incorporation of AI technology has auspicious prospects for augmenting agricultural output, sustainability, and adaptability to fluctuating environmental and financial constraints. Satellite remote sensing, combined with deep learning, can estimate the crop yield quite accurately. In this paper, we discuss the use of deep learning techniques with time series analysis to estimate crop yield from remote sensing technology. We explore how to combine convolutional neural networks (CNN) and recurrent neural networks (RNN) with traditional time series analytical techniques to exploit spatial patterns found in satellite images and historical crop production data. Experiments conducted on practical agricultural datasets demonstrate the utility of the proposed framework and highlight its potential for accurate and timely crop production forecasting.

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Utilizing a fusion of remote sensing data and machine learning models to forecast flood risks to agriculture in Hanoi City, Vietnam

Cited by 3 publications

References 44 publications

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Quantifying rice dry biomass to determine the influence of straw burning on BC and NO2 emissions in the Hanoi metropolitan region

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Fusion of Deep Learning Techniques With Time Series Analysis for Crop Yield Prediction From Satellite Remote Sensing Data

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