Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning

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.

Plant phenotyping relevance

衛星画像と機械学習により、バナナの病害に伴う植生指数の異常を推定する手法を開発・適用しており、感染植物の状態を大規模に評価することが中心です。

Code and data availability

The paper's Data Availability Statement explicitly deposits analysis code and data in a public GitHub repository by the authors, covering the remote-sensing/ML phenotyping analysis.