[HTML][HTML] Computer vision in smart agriculture and precision farming: Techniques and applications
The transformation of age-old farming practices through the integration of digitization and
automation has sparked a revolution in agriculture that is driven by cutting-edge computer …
automation has sparked a revolution in agriculture that is driven by cutting-edge computer …
A comparative review on multi-modal sensors fusion based on deep learning
The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
data with characteristics of high volume, wide variety, and high integrity. However, traditional …
AutoML-GWL: Automated machine learning model for the prediction of groundwater level
Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective
water resource governance. However, groundwater level prediction is intricate, driven by …
water resource governance. However, groundwater level prediction is intricate, driven by …
Optimizing Irrigation Efficiency with IoT and Machine Learning: A Transfer Learning Approach for Accurate Soil Moisture Prediction
This research aims to develop a Machine Learning model for predicting soil moisture levels,
which may be used to construct smart irrigation systems. The model was evaluated and …
which may be used to construct smart irrigation systems. The model was evaluated and …
Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
Fedfusion: Manifold driven federated learning for multi-satellite and multi-modality fusion
Multi-Satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion
representation of complex high-dimensional data under limited computational resources …
representation of complex high-dimensional data under limited computational resources …
Strategies to measure soil moisture using traditional methods, automated sensors, remote sensing, and machine learning techniques: review, bibliometric analysis …
This review provides a detailed synthesis of various in-situ, remote sensing, and machine
learning approaches to estimate soil moisture. Bibliometric analysis of the published …
learning approaches to estimate soil moisture. Bibliometric analysis of the published …
Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models
Soil Moisture (SM) monitoring is crucial for various applications in agriculture, hydrology,
and climate science. Remote Sensing (RS) offers a powerful tool for large-scale SM …
and climate science. Remote Sensing (RS) offers a powerful tool for large-scale SM …
A machine learning approach to predict the k-coverage probability of wireless multihop networks considering boundary and shadowing effects
Network coverage is a pivotal performance metric of wireless multihop networks (WMNs)
determining the quality of service rendered by the network. Earlier, a few studies have …
determining the quality of service rendered by the network. Earlier, a few studies have …
[HTML][HTML] Leveraging internet of things and emerging technologies for earthquake disaster management: Challenges and future directions
Seismology is among the ancient sciences that concentrate on earthquake disaster
management (EQDM), which directly impact human life and infrastructure resilience. Such a …
management (EQDM), which directly impact human life and infrastructure resilience. Such a …