The role of image-based phenotyping tools in terms of disease treatment management

The Mediterranean agriculture sector is vulnerable to abiotic and biotic stresses of diverse nature, with wheat (Triticum aestivum. L.) being by area extension one of the most important crops. In this sense, it is especially important to identify the most appropriate tools for monitoring the impact of drought and fungal diseases on wheat yield. The purpose of our research is to develop a prediction model for crop monitoring and phenotyping purposes using RGB Vegetation Indexes (VIs), through ground-acquired RGB images. The current study assessed forty advanced winter wheat accessions at four experimental sites in NE Spain: Briviesca, Ejea de los Caballeros, Sos del Rey Católico and Tordómar, receiving using 15 VIs computed from RGB (Red-Green-Blue) bands. The sites received full/moderate irrigation support or rainfed only. For each date and treatment data subset, VIs or their associated Principal Components (PC) was analyzed. In light of this, an interesting range separation between the treated and untreated groups at several different sites was observed. Furthermore, the use of sprinkler irrigation resulted in minor fungal pressure due to its lower likelihood of fungal dispersion. As a conclusion, we realized that the variations in RGB VIs over the growing season can be used as user-friendly, time-efficient and cost-effective tools to distinguish different growth stages/phenologies and largely for disease prediction in order to assess fungicide treatment efficacy among wheat varieties, across different experimental sites. Moreover, differences in weather and other site differences, such as irrigation method or a lack of, seem to have impacts on fungicide pressure.