aceComparative evaluation of machine learning algorithms for Maize Lethal Necrosis Disease detection under class imbalance
Maize Lethal Necrosis Disease (MLND) remains a major threat to maize production in sub-Saharan Africa, creating a need for accurate, interpretable, and computationally efficient diagnostic tools. This study presents a comprehensive comparative evaluation of nine machine learning algorithms for MLND detection using a structured feature-based dataset comprising 7,892 maize leaf observations obtained from the Kenya Agricultural and Livestock Research Organization (KALRO). The extracted features captured color, text…
Structured leaf-feature models can detect maize lethal necrosis with consistent, interpretable and computationally light performance, with LightGBM offering the best balance for low-resource settings.
The achieved performance means about one in four samples may be misclassified, and the models need further external validation before operational deployment.
Evidence
- Peer-reviewedPLOS One2026-10-23
How should this claim be treated?
Truvace Impact Record TRV-2026-2242, v1: “Comparative evaluation of machine learning algorithms for Maize Lethal Necrosis Disease detection under class imbalance.” Truvace, 2026-10-24. /record/TRV-2026-2242 (accessed at citation time). sha256 09e938b7a353f9eb…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2242 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.