Satellite Earth Observation (EO) for agriculture

Gerald Blasch (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MONITORING AGRICULTURE SATELLITE OBSERVATION EARTH OBSERVATION SATELLITES

Wheat rust early warning systems in Ethiopia: using new technologies to combat crop disease

Gerald Blasch (2020)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS MONITORING DISEASE SURVEILLANCE EARLY WARNING SYSTEMS

Sen4Rust: sentinel satellites for wheat rust disease forecasting

Gerald Blasch (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MONITORING SATELLITE OBSERVATION RUSTS FORECASTING

KARC 2020 Experiment – The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING WHEAT CROPS DISEASES

Wheat rust early warning systems in Ethiopia: implementation of remote sensing technologies to combat crop disease

Gerald Blasch (2020)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS MONITORING DISEASE SURVEILLANCE EARLY WARNING SYSTEMS REMOTE SENSING

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch David Hodson Francelino Rodrigues (2023)

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Artículo

Very High Resolution Imagery Disease Detection Methods Early Growth Stages CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA UNMANNED AERIAL VEHICLES STEM RUST PHENOTYPING HIGH-THROUGHPUT PHENOTYPING WHEAT

Phenotyping with satellites

Urs Schulthess Gerald Blasch Francisco Pinto Mainassara Zaman-Allah (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SATELLITE OBSERVATION PHENOTYPING SATELLITE IMAGERY MONITORING

Remote sensing of quality traits in cereal and arable production systems: A review

Zhenhai  Li xiuliang jin Gerald Blasch James Taylor (2024)

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Artículo

Quality Traits Grain Protein CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING QUALITY GRAIN PROTEINS CEREALS PRODUCTION SYSTEMS