A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
2025 · Smart Agricultural Technology · 28 citations
https://doi.org/10.1016/j.atech.2025.100879
N
Namal Rathnayake
Claim or correct this author profile
Claim this profile or suggest corrections to the name, affiliation, bio, photo, or paper titles. Approved changes appear as verified CitedEvidence overlays.
53
Papers
750
Citations
15
h-index
20
i10-index
Namal Rathnayake is an academic researcher from Japan Agency for Marine-Earth Science and Technology. The author has contributed to research in topics: Hydrological Forecasting Using AI & Hydrology and Watershed Management Studies & Flood Risk Assessment and Management. The author has an h-index of 15, co-authored 46 publications.
ORCID: 0000-0002-5235-8552Papers by this author
worksArtificial intelligence to predict soil temperatures by development of novel model
2024 · Scientific Reports · 25 citations
https://doi.org/10.1038/s41598-024-60549-x
AI-driven predictions of geophysical river flows with vegetation
2024 · Scientific Reports · 13 citations
https://doi.org/10.1038/s41598-024-67269-2
Spatial mapping and analysis of forest fire risk areas in Sri Lanka – Understanding environmental significance
2024 · Case Studies in Chemical and Environmental Engineering · 9 citations
https://doi.org/10.1016/j.cscee.2024.100680
Artificial intelligence to evaluate the impact of urban green and blue spaces on chlorophyll-a concentrations
2025 · Environmental Science and Pollution Research · 1 citations
https://doi.org/10.1007/s11356-025-36292-9
Research topics
topicsChat about Author
Papers
Chat
Click to start Chat