Sorting biotic and abiotic stresses on wild rocket by leaf-image hyperspectral data mining with an artificial intelligence model.

ABSTRACT:

Background

Wild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies.

Methods

Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for train

SUBMITTER: Navarro A 

PROVIDER: S-EPMC8977030 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature