High-throughput plant phenotyping requires integrated image-based tools for automated and simultaneous quantification of multiple morphological and physiological traits, which are valuable indicators of plant sensitivity to limiting environmental conditions. In this study, we proposed a novel segmentation algorithm for the automatic collection of plant structural parameters based on three-dimensional (3D)-modeling obtained through a phenotyping platform and a Structure from Motion (SfM) approach. The algorithm was initially tested on a 3D-reconstruction of four potted commercial tomato cultivars, namely "Saint Pierre" (S), "Costoluto Fiorentino" (C), "Reginella" (R), and "Gianna" (G) for the identification of the main phenotypic plant traits (heights, angles and areas). The results pointed out that the proposed algorithm was able to automatically detect and measure the plant height (R2? = 0.98, RMSE? = 0.34 cm, MAPE? = 3.12% and AIC? = 6.03), petioles inclination (R2? = 0.96, RMSE? = 1.35?, MAPE? = 3.64% and AIC? = 22.16), single-Leaf Area (R2? = 0.98, RMSE? = 0.95 cm2, MAPE? = 7.40% and AIC? = 14.91) and single-leaf angle (R2? = 0.84, RMSE? = 1.43?, MAPE? = 2.17% and AIC? = 15.83). As a study case, the algorithm was applied for monitoring plant's dynamic responses to early water stress, measured according to Fraction of Transpirable Soil Water (FTSW), of the same tomato varieties, grown in pots, during 20 consecutive days under three treatments (full-irrigation, 50% deficit irrigation and no-irrigation). The results showed that for R and G cv., plant height was the phenotypic trait most sensitive to water stress (plant growth inhibition at 0.58 of FTSW value), while Total Leaf Area and transpiration rates started to be affected at lower FTSW (0.52 and 0.40, respectively). Conversely, S and C cv. did not exhibit any significant change in phenotypic traits under analysis, likely because these varieties exhibited a slow growth rate, allowing them to consume less water and therefore not reach a water stress threshold. The results indicated that plant height trait might be used in subsequent analyses to facilitate the rapid identification of tomato varieties resistant to water stress, thus enhancing crossbreeding programmes. ? 2022 Elsevier B.V.
Implementation of an algorithm for automated phenotyping through plant 3D-modeling: A practical application on the early detection of water stress
Moriondo M
2022
Abstract
High-throughput plant phenotyping requires integrated image-based tools for automated and simultaneous quantification of multiple morphological and physiological traits, which are valuable indicators of plant sensitivity to limiting environmental conditions. In this study, we proposed a novel segmentation algorithm for the automatic collection of plant structural parameters based on three-dimensional (3D)-modeling obtained through a phenotyping platform and a Structure from Motion (SfM) approach. The algorithm was initially tested on a 3D-reconstruction of four potted commercial tomato cultivars, namely "Saint Pierre" (S), "Costoluto Fiorentino" (C), "Reginella" (R), and "Gianna" (G) for the identification of the main phenotypic plant traits (heights, angles and areas). The results pointed out that the proposed algorithm was able to automatically detect and measure the plant height (R2? = 0.98, RMSE? = 0.34 cm, MAPE? = 3.12% and AIC? = 6.03), petioles inclination (R2? = 0.96, RMSE? = 1.35?, MAPE? = 3.64% and AIC? = 22.16), single-Leaf Area (R2? = 0.98, RMSE? = 0.95 cm2, MAPE? = 7.40% and AIC? = 14.91) and single-leaf angle (R2? = 0.84, RMSE? = 1.43?, MAPE? = 2.17% and AIC? = 15.83). As a study case, the algorithm was applied for monitoring plant's dynamic responses to early water stress, measured according to Fraction of Transpirable Soil Water (FTSW), of the same tomato varieties, grown in pots, during 20 consecutive days under three treatments (full-irrigation, 50% deficit irrigation and no-irrigation). The results showed that for R and G cv., plant height was the phenotypic trait most sensitive to water stress (plant growth inhibition at 0.58 of FTSW value), while Total Leaf Area and transpiration rates started to be affected at lower FTSW (0.52 and 0.40, respectively). Conversely, S and C cv. did not exhibit any significant change in phenotypic traits under analysis, likely because these varieties exhibited a slow growth rate, allowing them to consume less water and therefore not reach a water stress threshold. The results indicated that plant height trait might be used in subsequent analyses to facilitate the rapid identification of tomato varieties resistant to water stress, thus enhancing crossbreeding programmes. ? 2022 Elsevier B.V.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
