Machine Vision Approaches for Vegetable Seedling Growth Measurement

Lin, Tong-Bao; Chien, Chung-Fang; Liao, Wei

doi:10.17660/actahortic.2002.578.38

Cited by 2 publications

(2 citation statements)

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“…Image analysis may complement or reduce the need for destructive sampling if data obtained from digital images are significantly correlated with data from direct, destructive measures. However, correlations decline with canopy closure when leaves overlap and are underrepresented in image analysis (Lin et al, 2002). In this study, WinCAM software-mediated estimates of canopy size based on digital images correlated significantly with direct measures of aboveground fresh and dry weights, leaf area, stem diameter, and, less often, plant height when tested 12, 15, and 18 d after sowing (Table 5).…”

Section: Discussionmentioning

confidence: 58%

A New Method to Estimate Vegetable Seedling Vigor, Piloted with Tomato, for Use in Grafting and Other Contexts

Bennett

Kleinhenz

2016

hortte

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The primary objective of this study was to test an improved method for estimating vegetable seedling vigor, which is important in grafting and other contexts. The study was also designed to test correlations between destructive and nondestructive measures of seedling growth and the effect of tomato (Solanum lycopersicum) rootstock and scion seedling vigor on graft success. Emergence and biomass accumulation and distribution of 18 tomato rootstock and five scion cultivars were monitored in the greenhouse through 18 days after sowing using seven destructive and nondestructive measures; growing conditions were also monitored. Plant and environmental data were used: 1) to develop cultivar growth curves, rank-sum values, and multicomponent seedling vigor values, and 2) to test correlations between percent canopy cover and other foliar measures. Also, seedlings representing all 90 rootstock–scion combinations and their associated seedling vigor values were cleft-grafted using accepted methods and grafted-plant survival was evaluated 2 weeks later. The experiment was conducted twice. Overall seedling vigor and its components differed significantly between runs of the experiment and among cultivars, although most cultivars had similar rankings (relative vigor) in both runs. Rank-sum and seedling vigor values ordered cultivars similarly. However, the range of cultivar seedling vigor values (3–11,504) greatly exceeded the range of rank-sum values (4–92). Correlations between destructive and nondestructive measures were significant. Graft success did not differ among cultivar combinations. We conclude that 1) the method to estimate seedling vigor described herein is useful in grafting and other contexts, including when discerning cultivar and other treatment effects, 2) nondestructive measures can substitute for some destructive ones, and 3) graft success in tomato is unrelated to rootstock and scion seedling vigor, provided proper grafting and healing techniques and commercial cultivars are used.

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Section: Discussionmentioning

confidence: 58%

A New Method to Estimate Vegetable Seedling Vigor, Piloted with Tomato, for Use in Grafting and Other Contexts

Bennett

Kleinhenz

2016

hortte

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“…For the detection of seedling growth status, an early method could be found in Ling and Ruzhitsky (1996), which could measure tomato seedling canopy with an adaptive threshold algorithm and the Otsu method. Lin et al estimated the leaf area of seedlings by the projected contour image and proposed an image processing method based on elliptic Hough transform to determine the overlapping position of seedlings leaves (Lin et al, 2002). A model for estimating seedling leaf area was developed using vision technology in Karimi (2009), where the model used the linear regression equation of leaf length and width obtained by a vision to estimate the leaf area.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

et al. 2021

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Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

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Machine Vision Approaches for Vegetable Seedling Growth Measurement

Cited by 2 publications

References 0 publications

A New Method to Estimate Vegetable Seedling Vigor, Piloted with Tomato, for Use in Grafting and Other Contexts

A New Method to Estimate Vegetable Seedling Vigor, Piloted with Tomato, for Use in Grafting and Other Contexts

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

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