Year: 2025
Type: article
Abstract: Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but there is a need for formal analysis to compare inferred with observed disease data. Here we use Landsat-8 data to investigate the detection of two banana diseases: banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). We use satellite imagery to develop meteorology-driven predictive models for vegetation phenology, specifically based on healthy crops. Machine learning is then applied to identify anomalies associated with diseased plants by comparing the predicted vegetation indices of healthy crops with the observed indices from published data when disease is present. Our results show a correlation between changes in vegetation indices and the number of infected cases, highlighting the potential of this approach for large-scale disease surveillance.
Source: Remote Sensing
DOI: 10.3390/rs17132308
Institutions: University of Cambridge, The University of Queensland
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4412067563