Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery

Precision nitrogen fertilizer application depends on accurate estimation of plant nitrogen content. However, the assessment of plant nitrogen content at early growth stages of paddy rice through remote sensed images is complicated by the compound effects of backgrounds (e.g. flood water, bare soil, algae, etc.) on the band reflectance. The rapid changing of plant nitrogen content during the vegetative phase makes the development of an operational prediction model very difficult. In this study, aerial images acquired by a quadcopter unmanned aerial vehicle (UAV) equipped with a multispectral sensor were used to estimate plant nitrogen content at vegetative phase of rice crops. The experiments were conducted at the experimental farm of Taiwan Agricultural Research Institute (TARI) from 2018 to 2020. A variable, N-index (ratio between N content of plants to be evaluated and plants not receiving N fertilizers), was introduced to resolve the issues related to rapid changing of plant N content during the vegetative phase. After removing the interference on band reflectance by background from the aerial images, the most appropriate vegetation indices and period that can capture the variations of N-index of rice plants were identified. It was found that a normalized difference red edge index (NDRI) and red edge chlorophyll index (RECI) based model correlated well with the N-index values from c.a. 30 days after transplanting (DAT) to 55 DAT (i.e., the most crucial period for rice yield and grain quality). The developed model was then used to display the spatial and temporal heterogeneity in plant nitrogen status within an experimental field as an example to illustrate how to use the model. In the example, soil plant analysis development (SPAD) meter values at locations of various levels of estimated N-index were collected as surrogates of plant nitrogen content at various DATs to build relationships for converting N-index maps to SPAD maps for potential variable rate fertilizer application management.