Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

• Main Authors: Dimitris Stavrakoudis, Dimitrios Katsantonis, Kalliopi Kadoglidou, Argyris Kalaitzidis, Ioannis Z. Gitas
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Remote Sensing
• Subjects: rice agronomic traits; multispectral UAV imagery; nitrogen uptake; nitrogen concentration; yield; aboveground biomass; multiple linear regression modeling; lasso input selection
• Online Access: http://www.mdpi.com/2072-4292/11/5/545

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.