Physiological and Digital Phenotyping of Drought Tolerance in Brassica Crops

Abstract

Climate change poses a significant threat to agricultural systems, with drought becoming increasingly prevalent in the Canadian prairies. This study addresses the urgent need to enhance crop resilience, focusing on Brassica carinata, a promising industrial feedstock crop used for the production of biofuels. Our research aims to comprehensively evaluate drought adaptive capacity in B. carinata through a combination of physiological and digital phenotyping methods. Under controlled conditions, we utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm. This system facilitated precise measurements of physiological traits, soil conditions, and atmospheric parameters, enabling the assessment of drought response. Concurrently, we conducted a field phenotyping experiment with 47 B. carinata Nested Association Mapping (NAM) founder lines and two B. napus checks, under irrigated and non-irrigated conditions. Aerial imagery obtained through Unmanned Aerial Vehicles (UAVs), complemented by phenological observations and manually recorded phenotypic data, was systematically gathered. Digital phenotypes extracted from aerial images are analyzed to identify a digital phenotype(s) for drought tolerance. Our study also explores the correlation between indoor physiological data and field performance of B. carinata lines, in an effort to identify parameters that can serve as reliable predictors of seed yield under drought stress. Overall, we believe this research provides valuable insights for enhancing crop resilience to drought.

Plant phenotyping relevance

Plantarray高スループット生理フェノタイピングとUAV画像からのデジタル形質抽出が、乾燥耐性評価の中心的手法として明示されている。

Code and data availability

The supplied blocks are only the title/abstract pages of a NAPPN conference abstract preprint. They describe Plantarray physiological screening and UAV-based field phenotyping of B. carinata, but contain no data availability statement, no public dataset or image deposit, no author analysis code or model release, and no

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