UAV-Based Hyperspectral Imaging Technique to Estimate Canola (Brassica napus L.) Seedpods Maturity

Abstract

Identification of optimal pod maturity stage in Canola is key for maximizing seed yield, quality and also an important phenotypic trait in crop improvement programs. The conventional method is via visual inspection of seed color change. Alternatively, hyperspectral sensors have potential to determine physiological status of the crops. Therefore, the objective of the study is to estimate canola seed maturity using field-based hyperspectral imaging. For this study, five canola genotypes (NAM-0, NAM-13, NAM-17, NAM-48, and NAM-76) were selected from an experiment of 56 populations. The experimental field was imaged using a UAV-mounted hyperspectral camera (400–1,000 nm) at five growth stages starting from pod formation to near-harvest maturity (BBCH-79(S1) to 88(S5)). For each genotype; pod and seed moisture were estimated on the same day of imaging. First-order derivative was conducted on reflectance data to determine optimal spectral wavebands. As a part of this study, a new vegetation index denoted “Canola-Pod-Maturity Index (CPMI)” was developed. CPMI was evaluated in comparison with four existing vegetation indices (mNDRE, PSRI, MCARI, WBI). CPMI showed a stronger relationship ( 0.81–0.98 for pods and 0.66–0.85 for seeds) with pod and seed moisture for all the genotypes. Furthermore, the new index was able to find differences among genotypes with variable maturity times.