Machine learning applications in agriculture: current trends, challenges, and future perspectives
Progress in agricultural productivity and sustainability hinges on strategic investments in
technological research. Evolving technologies such as the Internet of Things, sensors …
technological research. Evolving technologies such as the Internet of Things, sensors …
Selecting and Evaluating Key MDS-UPDRS Activities Using Wearable Devices for Parkinson's Disease Self-Assessment
Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This
disease has no cure, but assessing these motor symptoms will help slow down that …
disease has no cure, but assessing these motor symptoms will help slow down that …
Informative relationship multi-task learning: Exploring pairwise contribution across tasks' sharing knowledge
Multi-task learning is a machine learning paradigm, that aims to leverage useful domain
information to help improve the generalization performance of all tasks. Learning the …
information to help improve the generalization performance of all tasks. Learning the …
Web service framework to identify multiple pollutions in potential contaminated sites
X Lu, J Du, G Wang, X Li, L Sun, Y Zhang… - Expert Systems with …, 2025 - Elsevier
The traditional site environmental investigation and pollution identification mainly relies on
manual filling of paper forms and experience-weighted scoring, which have limitations in …
manual filling of paper forms and experience-weighted scoring, which have limitations in …
Artificial intelligence for assessing organic matter content and related soil properties
R Lal - Journal of Soil and Water Conservation, 2024 - Taylor & Francis
Settled farming or agriculture has un-dergone several revolutionary periods ever since its
start about 10 millennia ago (Jellason et al. 2021; Akinkuotu 2023). The first Agricultural …
start about 10 millennia ago (Jellason et al. 2021; Akinkuotu 2023). The first Agricultural …
Precision Fertilization Via Spatio-temporal Tensor Multi-task Learning and One-Shot Learning
Precision fertilization is essential in agricultural systems for balancing soil nutrients,
conserving fertilizer, decreasing emissions, and increasing crop yields. Access to …
conserving fertilizer, decreasing emissions, and increasing crop yields. Access to …
ParallelFarm: An AI-Enabled Sustainable Farming Management System for Carbon Neutrality
Promoting sustainable agriculture plays a crucial role in reducing greenhouse gas (GHG)
emissions, lowering the carbon footprint, and improving farm resilience. Three challenges …
emissions, lowering the carbon footprint, and improving farm resilience. Three challenges …
Implementing machine learning technology for smart agriculture based on data collection systems and predictive analytics use cases
R Ramful, Y Beeharry - Artificial Intelligence based Solutions for …, 2024 - taylorfrancis.com
The need for improved efficiency in the agricultural sector in the 21st century is
indispensable to address the numerous challenges, which are faced by farmers in the sector …
indispensable to address the numerous challenges, which are faced by farmers in the sector …
The Most Important Predictors of Fertiliser Costs
VJPD Martinho - … Approaches for Evaluating Statistical Information in the …, 2024 - Springer
The control of the fertiliser costs in the agricultural sector is fundamental for the profitability of
the farms and to mitigate environmental impacts. Indeed, the fertiliser costs have, at least …
the farms and to mitigate environmental impacts. Indeed, the fertiliser costs have, at least …
Investigation of Multi-dimensional Tensor Multi-task Learning for Modeling Alzheimer's Disease Progression
Y Zhang - 2024 - etheses.whiterose.ac.uk
Machine learning (ML) techniques for predicting Alzheimer's disease (AD) progression can
significantly assist clinicians and researchers in constructing effective AD prevention and …
significantly assist clinicians and researchers in constructing effective AD prevention and …