Okra ( Abelmoschus esculentus L.) is an important vegetable crop grown extensively across tropical and subtropical areas, but its yield is highly impacted by yellow vein mosaic virus (YVMV) and okra enation leaf curl virus (OELCV), causing yield loss of up to 90%. In this research, 16 heterogeneous okra genotypes and 21 F 1 hybrids from a 7 × 7 half‐diallel mating scheme are tested across three growing seasons on morphological, biochemical, and resistance to disease traits. Fuzzy C‐means clustering and regression modeling are coupled with field phenotyping to sort genotypes along the course of disease development and forecast the onset of disease. The fuzzy classification method successfully captures intermediate resistance reactions, increasing the accuracy of genotype selection. Regression analysis detects genotypes with reduced symptom expression, notably YVMV, and their potential in resistance breeding is highlighted. The integrated approach exemplifies the capability of AI‐aided phenotyping in expediting the generation of virus‐tolerant okra cultivars via accurate and early‐stage selection.
Roy et al. (Mon,) studied this question.