Design of an AI-Driven Analytical Framework Integrating Machine Learning and Hyperspectral Remote Sensing for Detection and Classification of Locust-Prone Areas in South Nyanza Kenya

Emmanuel Ochako  Manyange; Juliana  Kamaghe; lilian Mutalemwa

doi:10.51903/jtie.v5i2.560

Authors

Emmanuel Ochako Manyange Dept. of Information Technology, Mount Kigali University, Rwanda
Juliana Kamaghe Dept. of Maths and ICT, Open University of Tanzania, Tanzania https://orcid.org/0009-0008-0917-2366
lilian Mutalemwa Dept. of Maths and ICT, Open University of Tanzania, Tanzania https://orcid.org/0000-0003-4342-5562

DOI:

https://doi.org/10.51903/jtie.v5i2.560

Keywords:

Artificial Intelligence, Machine Learning, Hyperspectral Remote Sensing, Locust Classification, Early Warning Systems

Abstract

Desert locust outbreaks pose a persistent threat to agricultural productivity and food security in East Africa, while conventional surveillance approaches remain limited by delayed reporting, restricted spatial coverage, and weak predictive capability. This study develops and evaluates an artificial intelligence-driven analytical framework that integrates machine learning with hyperspectral remote sensing for detecting and classifying locust-prone areas in South Nyanza, Kenya. An empirical quantitative experimental design was applied to 150 georeferenced spatial observation units using spectral, vegetation, and bioclimatic indicators derived from Sentinel-2, CHIRPS, and MODIS data. The analytical framework incorporated automated feature engineering, Random Forest classification, Logistic Regression, and geospatial hazard visualization. The Random Forest model, configured with 500 decision trees and an mtry value of 3, achieved an overall classification accuracy of 81.33%, precision of 81.58%, recall of 81.58%, F1-score of 81.58%, and an Out-of-Bag error rate of 18.67%. The validated ROC-AUC reached 0.835, indicating good discrimination capability. Variable importance analysis identified precipitation, soil moisture represented by SAVI, and vegetation greenness represented by NDVI as the most influential predictors. Logistic Regression showed a positive association between hyperspectral indicators and locust-prone classification, although the predictors were not statistically significant at the 5% level. The findings demonstrate that integrating machine learning with remotely sensed environmental indicators provides a scalable approach for strengthening locust surveillance and supporting evidence-based early warning systems.

References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Cressman, K. (2013). Climate change and locusts in the WANA region. Agronomy, 3(4), 808–823. https://doi.org/10.3390/agronomy3040808

Cressman, K. (2016). Monitoring desert locusts in the Middle East: An overview of surveillance systems. Journal of Applied Entomology, 140(8), 575–582. https://doi.org/10.1111/jen.12289

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications.

Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

Food and Agriculture Organization. (2021). Desert locust situation update. Food and Agriculture Organization.

Food and Agriculture Organization. (2022). Desert locust monitoring and early warning systems in Eastern Africa. Food and Agriculture Organization.

Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of thematic maps obtained by image classification. Remote Sensing of Environment, 239, 111630. https://doi.org/10.1016/j.rse.2019.111630

Gbegbelegbe, S., Koffi, E., & Sultan, B. (2021). Climate variability and agricultural pest outbreaks in Africa: Implications for food security. Environmental Research Letters, 16(5), 054017.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. (2013). Applied logistic regression (3rd ed.). Wiley.

Leitner, S., Kraxner, F., & Liu, J. (2022). Digital agriculture and artificial intelligence adoption for sustainable agricultural management. Agricultural Systems, 196, 103343.

Lazar, M., Tóth, G., & Jones, A. (2020). Monitoring environmental change using geospatial technologies for agricultural management. Remote Sensing Applications: Society and Environment, 20, 100407.

Makori, D. M., Muthoni, F., & Ochieng, J. (2019). Technology adoption in agricultural systems: Factors influencing acceptance of digital monitoring systems. International Journal of Agricultural Extension, 7(2), 87–98.

Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817.

Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.

Mubarak, S., Ahmed, M., & Hassan, R. (2022). Machine learning applications in agricultural monitoring systems: Recent developments and future directions. Remote Sensing Applications: Society and Environment, 25, 100678.

Olden, J. D., Lawler, J. J., & Poff, N. L. (2008). Machine learning methods without tears: A primer for ecologists. Quarterly Review of Biology, 83(2), 171–193.

Powers, D. M. W. (2020). Evaluation metrics for machine learning models, classification, and prediction. Journal of Machine Learning Technologies, 2(1), 37–63.

Salih, A. A., Baraibar, M., Mwangi, K., & Artan, G. (2020). Climate change and locust outbreak in East Africa. Scientific Reports, 10(1), 15384. https://doi.org/10.1038/s41598-020-68895-2

Shafiee, S., Lied, L. M., Burud, I., Dieseth, J. A., & Alsadon, A. (2021). Hyperspectral imaging for monitoring agricultural systems: A review. Remote Sensing, 13(14), 2750. https://doi.org/10.3390/rs13142750

Singh, A., Ganapathysubramanian, B., Sarkar, S., & Mueller, D. (2018). Deep learning for plant stress phenotyping: Trends and future perspectives. Trends in Plant Science, 23(10), 883–898.

Thenkabail, P. S., Lyon, J. G., & Huete, A. (2018). Hyperspectral remote sensing of vegetation. CRC Press.

Weiss, G. M. (2013). Foundations of imbalanced learning. In H. He & Y. Ma (Eds.), Imbalanced learning: Foundations, algorithms, and applications (pp. 13–41). Wiley.

Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1353691. https://doi.org/10.1155/2017/1353691

Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5