Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning
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 formal analysis is needed to compare inferred disease data w...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849427545466863616 |
|---|---|
| author | Renata Retkute Kathleen S. Crew John E. Thomas Christopher A. Gilligan |
| author_facet | Renata Retkute Kathleen S. Crew John E. Thomas Christopher A. Gilligan |
| author_sort | Renata Retkute |
| collection | DOAJ |
| description | 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 formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. |
| format | Article |
| id | doaj-art-577e94684e154989a09bf146a14a6ffa |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-577e94684e154989a09bf146a14a6ffa2025-08-20T03:28:59ZengMDPI AGRemote Sensing2072-42922025-07-011713230810.3390/rs17132308Detection of Banana Diseases Based on Landsat-8 Data and Machine LearningRenata Retkute0Kathleen S. Crew1John E. Thomas2Christopher A. Gilligan3Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, GPO Box 267, Brisbane, QLD 4001, AustraliaQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, GPO Box 267, Brisbane, QLD 4001, AustraliaEpidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UKBanana 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 formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection.https://www.mdpi.com/2072-4292/17/13/2308remote sensingplant disease detectionLandsatmachine learning |
| spellingShingle | Renata Retkute Kathleen S. Crew John E. Thomas Christopher A. Gilligan Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning Remote Sensing remote sensing plant disease detection Landsat machine learning |
| title | Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning |
| title_full | Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning |
| title_fullStr | Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning |
| title_full_unstemmed | Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning |
| title_short | Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning |
| title_sort | detection of banana diseases based on landsat 8 data and machine learning |
| topic | remote sensing plant disease detection Landsat machine learning |
| url | https://www.mdpi.com/2072-4292/17/13/2308 |
| work_keys_str_mv | AT renataretkute detectionofbananadiseasesbasedonlandsat8dataandmachinelearning AT kathleenscrew detectionofbananadiseasesbasedonlandsat8dataandmachinelearning AT johnethomas detectionofbananadiseasesbasedonlandsat8dataandmachinelearning AT christopheragilligan detectionofbananadiseasesbasedonlandsat8dataandmachinelearning |