Performance Evaluation and Validation of an AI-Driven Hyperspectral Remote Sensing Framework for Locust Surveillance Using Ground-Truth Data

Emmanuel Ochako  Manyange; Juliana  Kamaghe; Lilian  Mutalemwa

doi:10.51903/jtie.v5i2.561

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.v5i2.561

Keywords:

Artificial Intelligence, Hyperspectral Remote Sensing, Random Forest, Locust Surveilance, Model Validation

Abstract

Desert locust outbreaks continue to threaten agricultural productivity and food security across East Africa, creating an urgent need for surveillance systems that are accurate, scalable, and capable of supporting early intervention. This study evaluates and validates an AI-driven hyperspectral remote sensing framework for locust surveillance in South Nyanza, Kenya, using ground-truth observations and conventional field-scouting records as benchmarks. The study employed 150 georeferenced spatial units, with 70% used for model training and 30% reserved for independent validation. Random Forest and Logistic Regression were applied to hyperspectral and environmental indicators, while model performance was assessed using confusion matrix metrics, Out-of-Bag error, and Receiver Operating Characteristic analysis. On the 45-unit validation set, the AI-driven framework correctly classified 37 locations, achieving an accuracy of 82.22%, precision of 83.33%, recall of 83.33%, and an F1-score of 83.33%, with an Out-of-Bag error rate of 18.20%. The Random Forest model achieved an ROC-AUC of 0.844, substantially higher than the 0.585 obtained from the conventional field-scouting baseline. The framework also reduced false-negative detections from 10 to 4 cases. These findings demonstrate that integrating machine learning with hyperspectral remote sensing can strengthen locust surveillance by improving classification reliability, reducing missed infestations, and supporting evidence-based early warning and intervention planning.

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. FAO.

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

Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy 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.

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