Multi-temporal estimation of vegetable crop biophysical parameters with varied nitrogen fertilization using terrestrial laser scanning

Abstract Estimation of biophysical parameters at various crop growth stages is vital for precision agricultural crop production. Spatial delineation of crops’ responses to various levels of nutrients helps optimise resources and reduce nutrient leaching. This paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels. Experimental setup of growing three vegetable crops: tomato (Solanumlycopersicum L.), eggplant (Solanummelongena L.) and cabbage (Brassica oleracea L.) with three levels of N fertilization was laid out at the University of Agricultural Sciences, Bengaluru, India in 2017. LiDAR point clouds using a terrestrial laser scanner were collected at different growth stages. A methodology which included, among other processing steps, adaptive spatial filtering, canopy height modelling, watershed segmentation, and support vector regression has been adapted for the estimation of plant height, crown area, and biomass. Validation with ground measurements show high prediction accuracies for plant height (lowest coefficient of determination (R2), 0.96; highest symmetric mean absolute percentage error (SMAPE) of 3.18), and crown area (lowest R2, 0.82; highest SMAPE, 8.82) for all the three crops across growth stages. The combined use of plant height and the crown area has enabled accurate and consistent estimation of biomass (lowest R2, 0.92; highest SMAPE, 7.53) throughout the growing season. However, the mapping of a specific range of biomass to a specific N level is ambiguous due to wider variations in the crop growth due to rainfall, and wind interferences.