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Abstract. In agronomy, High-throughput Phenotyping (HTP) can provide key information for agronomist in genomic selection as well as farmers in yield prediction. Recently, HTP using Unmanned Aerial Systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts such as multispectral bands and early/late growth stages. The listed challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, the research team previously built a 3-step data processing pipeline, which is highly modular. For this project we present improvements to the previous methods used for canopy segmentation and crop plot localization for crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate advantages by comparing with processing results using the original model. Based on these proposed methods, we implement two MATLAB programs, namely Multi-Layer Mosaic version 2 (MLM2) and Vegetation Indices Derivation version 1 (VID1). Using MLM2 and VID1, we compute canopy coverage and Normalized Difference Vegetation Indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate water depression and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially on soybean reproductive stage approximation. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP in UAS to general plant breeding.