Estimativa da área foliar de plantas de lima ácida 'Tahiti' usando métodos não-destrutivos

Filho, M. A. Coelho; Angelocci, Luiz Roberto; Vasconcelos, M R B; Coelho, E. F.

doi:10.1590/s0100-29452005000100043

Cited by 16 publications

(4 citation statements)

References 15 publications

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“…In each defoliation, it was collected and counted all the leaves randomly taken from the canopy (Nl), measuring the maximum length (L) and maximum width (W) of 100 leaves. Mean values L and W were adopted to calculate the total leaf area of the tree (Equation 1) using a correction factor 'f ' equal to 0.7 (Coelho et al, 2005), allowing estimation of the initial value LA (without manual defoliation) and the determination of other post-defoliation values.…”

Section: Methodsmentioning

confidence: 99%

Área foliar e interceptação de radiação solar pela copa de uma laranjeira

Pilau

Angelocci

2015

Bragantia

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Leaf area changes affect solar radiation interception (K*), interception efficiency (ε int ) and extinction coefficient (k) of an orange tree top (cv. Pêra-Rio). In order to measure radiation transmitted through the crown a mobile sensor was horizontally installed below the crown and 0.65 m from the trunk, moving around it at 3 rpm. The model used for k determination (Monsi & Saeki theory) was assessed with independent data to estimate K*. With absence of leaves, it was detected an intense interference of trunk and branches on ε int , with a minimum value of 0.52. The results were also distinct in obtaining k, when the best fit was found with a quadratic rather than a linear equation, again influenced by woody structures. Simulation of K* using extinction coefficient (k) was statistically classified as good.

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Section: Methodsmentioning

confidence: 99%

Área foliar e interceptação de radiação solar pela copa de uma laranjeira

Pilau

Angelocci

2015

Bragantia

Self Cite

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“…Some methods and examples of how they are applied are cited in the literature, including the leaf disk method (Lopes et al, 2007), automatic planimeter (Mielke et al, 1995), leaf weight (Monteiro et al, 2005) and the gravimetric method (Norman and Campbell, 1989). Nondestructive methods include those that estimate leaf dimensions (Partelli et al, 2006), leaf areas using regression models (Moreira-Filho et al, 2007;Coelho-Filho et al, 2005) and those that use digital images and software for analysis. Methods involving the use of digital images can be destructive (if the leaves are removed from the plant) or non-destructive (if digital images of the leaves are captured without removal from the plant).…”

Section: Introductionmentioning

confidence: 99%

ImageJ software as an alternative method for estimating leaf area in oats

et al. 2021

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The aim of this study was to compare two methods of estimating leaf area (leaf area meter LI-COR 3100 and ImageJ software) in oats. Renascem black oats and UTF Iguaçu white oats cultivars were evaluated. All the leaves of one plant per plot, with six repetitions, were evaluated every seven days to obtain five assessments for each cultivar. The leaves were identified and fixed on a white A4 sheet of paper with an indication of a known area (45 cm²), photographed and evaluated using a leaf area meter (LI-3100 - LI-COR). Leaf area was then estimated using ImageJ software. Estimates were obtained for standard deviation and coefficient of variation, and a simple linear regression equation was estimated based on the two estimation methods. Mean variances were tested using the ‘F’-test and the means compared by the ‘t’-test. There was no difference between the leaf areas found by both methods. In addition, they were highly correlated, and variances were homogeneous. We concluded that ImageJ software can be used instead of the leaf area meter on the two oat cultivars evaluated.

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“…Close-range data collection became a subject of study with the popularization of portable sensors, as successfully shown in Koenig et al (2015) or Vázquez-Arellano et al (2016). Some studies focused on the use portable sensors in fruit growth monitoring using either active (Berk et al, 2016;Colaço et al, 2017;Escolà et al, 2017) or passive remote sensing sensors (Coelho Filho et al, 2005;Zhou et al, 2012;Linker, 2017). Given their high cost, these systems are usually not affordable for small holder farms and production estimates in several orchards are still based on visual counting techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

et al. 2021

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This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina's Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil's apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I 1 , I 2 , I 3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I" 2 , I" 3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I" 2 , I" 3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed

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Estimativa da área foliar de plantas de lima ácida 'Tahiti' usando métodos não-destrutivos

Cited by 16 publications

References 15 publications

Área foliar e interceptação de radiação solar pela copa de uma laranjeira

Área foliar e interceptação de radiação solar pela copa de uma laranjeira

ImageJ software as an alternative method for estimating leaf area in oats

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

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