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All Journal Journal of Technology Informatics and Engineering
Emmanuel Ochako Manyange
Dept. of Information Technology, Mount Kigali University, Rwanda
Computer Science & IT

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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
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.560

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.
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
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.561

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.
Co-Authors Juliana Kamaghe lilian Mutalemwa