Related papers: A Machine Learning Approach to Forecasting Remotel…
Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs.Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and…
A precise forecast for droughts is of considerable value to scientific research, agriculture, and water resource management. With emerging developments of data-driven approaches for hydro-climate modeling, this paper investigates an…
Over the last few decades, deforestation and climate change have caused increasing number of forest fires. In Southeast Asia, Indonesia has been the most affected country by tropical peatland forest fires. These fires have a significant…
The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However,…
Degraded rangelands undergo continual shifts in the appearance and distribution of plant life. The nature of these changes however is subtle: between seasons seedlings sprout up and some flourish while others perish, meanwhile, over…
With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to…
Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases the likelihood of extreme weather events and reduces the…
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivity and food security. Crop simulation models, which align…
The prediction of crop yields internationally is a crucial objective in agricultural research. Thus, this study implements 6 regression models (Linear, Tree, Gradient Descent, Gradient Boosting, K Nearest Neighbors, and Random Forest) to…
Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant…
Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To…
This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile,…
Meeting the increasing global demand for food security and sustainable farming requires intelligent crop recommendation systems that operate in real time. Traditional soil analysis techniques are often slow, labor-intensive, and not…
Leaf wetness detection is a crucial task in agricultural monitoring, as it directly impacts the prediction and protection of plant diseases. However, existing sensing systems suffer from limitations in robustness, accuracy, and…
Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction,…
Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to…
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a…
Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they…
Healthy urban forests comprising of diverse trees and shrubs play a crucial role in mitigating climate change. They provide several key advantages such as providing shade for energy conservation, and intercepting rainfall to reduce flood…
Wildfires pose significant threats to ecosystems, economies, and communities worldwide, necessitating advanced predictive methods for effective mitigation. This study introduces a novel and comprehensive dataset specifically designed for…