Leaf area (LA) and SPAD value (a proxy for chlorophyll content) are two key determinants of seedling quality. This study aimed to develop and validate approaches for the efficient retrieval of these features in order to facilitate both management and screening practices. In cucumber, different models were developed and tested for the accurate estimation of LA at the scale of the individual organ (cotyledon, leaf) by using its linear dimensions (length (L) and width (W)), and of the whole seedling by using the 2D image-extracted projected area (from three different angles: 0°, 45°, and 90°). At either scale, the SPAD value was computed by using image (90°)-based colorimetric features. The estimation of individual organ area was more accurate when using L alone, compared with W alone. By using the two dimensions and specific colorimetric features, the individual organ area (R2 ≥ 0.92) and SPAD value (R2 of 0.77) were accurately predicted. When considering a single view, the top one (90°) was associated with the highest accuracy in whole-seedling LA estimation, and the side view (0°) with the lowest (R2 of 0.88 and 0.73, respectively). Using any combination of two angles, the whole-seedling LA was accurately retrieved (R2 ≥ 0.88). When using colorimetric features, a poor estimation of the whole-seedling SPAD value was noted (R2 ≤ 0.43). The deployment of artificial neural networks (ANNs) further allowed the estimation of specific organ shape traits, and improved the accuracy of all the aforementioned predictions, including the whole-seedling SPAD value (R2 of 0.597). In conclusion, the findings of this study highlight that features readily retrieved from 2D images hold promising potential for improving screening routines within the nursery industry.
Tsaniklidis et al. (Sun,) studied this question.
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