Work

Joint prediction of potato yield and nitrogen status at row closure using multi-task deep learning and UAV-derived LiDAR–multispectral data

Year: 2026

Type: article

Abstract: • Fused UAV multispectral and LiDAR data quantified potato canopy structure and petiole nitrate-N at row closure. • Joint modelling of yield and petiole nitrate-N improved predictive performance compared with single-task approaches. • LiDAR height and structural metrics added complementary information to multispectral predictors. • Single-date UAV data at the row-closure stage were sufficient for nitrogen prediction but less informative for yield. • Feature attribution analyses indicated physiologically consistent relationships between canopy structure and nitrogen status.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2026.111893

Authors: Ehsan Chatraei Azizabadi, Nasem Badreldin

Institution: University of Manitoba

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,870