Visual assessment and computer–assisted image analysis of Fusarium head blight in the field to predict mycotoxin accumulation in wheat grains

• DOI: 10.1007/s10658-017-1345-z
• Publication Date: 2017-10-05T08:15:35Z
• Authors: Johann Leplat; Pierre Mangin; Laurent Falchetto; Cécile Heraud; Elodie Gautheron; Christian Steinberg
• Keywords: 2. Zero hunger; 0301 basic medicine; decision support tool; mycotoxine; fusariose de l'épi; trait phénotypique; computer assisted image analysis; analyse d'image; 630; 03 medical and health sciences; weather condition; preceding crop residue; blé; wheat; fusarium graminearum; [SDV.BV]Life Sciences [q-bio]/Vegetal Biology; [SDV.BV] Life Sciences [q-bio]/Vegetal Biology

Phenotypic traits are regularly used to diagnose the development of Fusarium head blight (FHB) in the field, whereas mycotoxin accumulation in wheat grains can only be accurately evaluated through costly methods, such as high–performance liquid chromatography (HPLC). The aim of this study was to determine whether: (i) the results provided by existing commercial decision support tools could be anticipated using phenotypic measurements, including a novel technique of computer–assisted image analysis of spikes; and (ii) these measurements could avoid using HPLC. We monitored the FHB development during two consecutive years in highly contaminated plots in the Burgundy region (France). Contamination by crop residues was simulated through a field inoculation with barley grains artificially colonized by Fusarium graminearum. The development of the disease on spikes and harvested grains was assessed on one tolerant and two susceptible wheat varieties. The accumulated amounts of mycotoxins were measured in harvested grains using HPLC. As expected, the measured traits revealed that the inoculum responsible for infection on spikes mainly came from residues left on the soil surface, and the susceptible varieties were more diseased than the tolerant variety. Weather conditions had a strong effect on disease development. The novel computer–assisted image analysis technique had a better prediction power of deoxynivalenol accumulation, was more objective and time–saving than classical visual symptom assessments. This assessment method could be suitable to supplement the use of existing prediction tools and might avoid systematic and costly mycotoxin measurements in likely infected plots.