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Remote Sensing Technology

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A new health prediction model for a sensor network based on belief rule base with attribute reliability.

Scientific reports
Health prediction plays an essential role in improving the reliability of a sensor network by guiding the network maintenance. However, affected by interference factors in the real operational environment, the reliability of the monitoring informatio...

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops.

Sensors (Basel, Switzerland)
In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of pl...

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.

Personalized medicine
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...

The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems.

Current opinion in biotechnology
Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current an...

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.

Sensors (Basel, Switzerland)
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the...

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Sensors (Basel, Switzerland)
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. ...

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.

Sensors (Basel, Switzerland)
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughp...

Change Detection of Remote Sensing Images Based on Attention Mechanism.

Computational intelligence and neuroscience
In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote se...

Yield prediction with machine learning algorithms and satellite images.

Journal of the science of food and agriculture
BACKGROUND: Barley is one of the strategic agricultural products available in the world, and yield prediction is important for ensuring food security. One way of estimating a product is to use remote sensing data in conjunction with field data and me...