[HTML][HTML] Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
Remote detection and monitoring of the vegetation responses to stress became relevant for
sustainable agriculture. Ongoing developments in optical remote sensing technologies have …
sustainable agriculture. Ongoing developments in optical remote sensing technologies have …
Remote sensing for agricultural applications: A meta-review
Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …
for human livelihood. Today, this role must be satisfied within a context of environmental …
Remote sensing in agriculture—accomplishments, limitations, and opportunities
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early
warning system, allowing the agricultural community to intervene early on to counter …
warning system, allowing the agricultural community to intervene early on to counter …
UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
S Fei, MA Hassan, Y Xiao, X Su, Z Chen, Q Cheng… - Precision …, 2023 - Springer
Early prediction of grain yield helps scientists to make better breeding decisions for wheat.
Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based …
Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based …
A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses
The global population is expected to reach 9.8 billion by 2050. There is an exponential
growth of food production to meet the needs of the growing population. However, the limited …
growth of food production to meet the needs of the growing population. However, the limited …
Uniting remote sensing, crop modelling and economics for agricultural risk management
The increasing availability of satellite data at higher spatial, temporal and spectral
resolutions is enabling new applications in agriculture and economic development …
resolutions is enabling new applications in agriculture and economic development …
Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches
Timely and reliable yield prediction at a large scale is imperative and prerequisite to prevent
climate risk and ensure food security, especially with climate change and increasing …
climate risk and ensure food security, especially with climate change and increasing …
[HTML][HTML] Soil C sequestration as a biological negative emission strategy
Soil carbon (C) sequestration in one of three main approaches to carbon dioxide removal
and storage through management of terrestrial ecosystems. Soil C sequestration relies of …
and storage through management of terrestrial ecosystems. Soil C sequestration relies of …
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
Wheat is the most important staple crop grown in Australia, and Australia is one of the top
wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is …
wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is …
Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
As the world's leading corn producer, the United States supplies more than 30% of the
global corn production. Accurate and timely estimation of corn yield is therefore essential for …
global corn production. Accurate and timely estimation of corn yield is therefore essential for …