An image processing based method was developed to measure volume and surface area of ellipsoidal agricultural products such as eggs, lemons, limes, and peaches. The method assumes that each product has an axi-symmetric geometry and is a sum of superimposed elementary frustums of right circular cones. The product volume and surface area are calculated as the sum of the volumes and surface areas of individual frustums using Matlab®. The dimensions of individual frustums are determined from a digitized picture of the product acquired by a Charged Coupled Device (CCD) camera and processed in Adobe Photoshop®. The volumes and surface areas computed showed good agreement with analytical and experimental results. The developed method proved to be accurate, precise, and easy to use.

Image processing method to determine surface area and volume of axi-symmetric agricultural products

https://opus.lib.uts.edu.au/bitstream/10453/117856/4/Do%20the%20plants%20in%20functional%20green%20walls%20contribute%20to%20their%20ability%20to%20filter%20particulate%20matter.pdf

Do the plants in functional green walls contribute to their ability to filter particulate matter

Grape (Vitis vinifera L.) yield and its quality are dependent of the status of the root system. Root distribution information is also valuable for soil and water management. An analysis of methods to evaluate the root distribution of grapevines for both, drip and microsprinkler irrigation in a Typic Acrustox is presented for the table grape cv. Italia grafted on the rootstock IAC-313, in northeastern Brazil. Measured root parameters using the monolith method were root dry weight (Dw) and root length density (Lv), while root area (Ap) was estimated using the soil profile method in combination with digital image analysis. For both irrigation systems, roots were present to the 1 m soil depth and extended laterally to 1 m distance from the trunk, but grapevines irrigated by microsprinkler showed greater root presence as the distance from the trunk increased. Values of Ap were reasonably well correlated to Dw and Lv. However, correlation values were higher when fractional root distribution was used. The soil profile method in combination with image analysis techniques, allows proper grapevine root distribution evaluation.

/pdf/grapevine-root-distribution-in-drip-and-microsprinkler-uixkzic5u0.pdf

Grapevine root distribution in drip and microsprinkler irrigation

Fine roots (diameter <0.30 mm) account for about 80% of total root length in many species but are often underestimated owing to their small size and near transparency. We have assessed a technique for rapid and accurate estimation of root length when using image analysis. To allow the collection of clear images, several stains were tested to determine when staining is beneficial. Plants of six species (barley (Hordeum vulgare L.), maize (Zea mays L.), oat (Avena sativa L.), soybean (Glycine max (L.) Merr.), strawberry (Fragaria × ananassa), and wheat (Triticum aestivum L.)) were grown for either 3 or 5 weeks after emergence. Roots of five woody species (honey locust (Gleditsia triacanthos L.), Norway maple (Acer platanoides L.), red ash (Fraxinus pennsylvanica Marsh.), silver maple (Acer saccharinum L), and hackberry (Celtis occidentalis L.)) were collected outdoors. Stains were evaluated over a range of concentrations and for three staining times. Root variables were measured with WinRHIZO, an interactiv...

Root contrast enhancement for measurement with optical scanner-based image analysis

Root morphology of youngGlycine max, Senna obtusifolia, andAmaranthus palmeri

Using area profile integration software and an image processing system, we reliably estimated total root surface area of intact each (Prunus persica (L.) Batsch.) seedlings by 1) producing high-quality monochromatic video images under preset and constant conditions; 2) determining a threshold gray intensity value that differentiates the finest roots on the image; 3) producing a binary image where all pixels with gray values above the threshold are black; 4) determining the proportion of black pixels on the 480× 512-pixel matrix; and 5) multiplying this two-dimensional root surface value by π to estimate total root surface area. Normalized intensity (an average intensity weighted according to the proportion of the binary image in each gray scale class) was calculated using software that superimposed the video image on the binary image and was used to estimate mean root diameter. Evidence of reliability and examples of the use of both estimates are provided.

/pdf/measuring-root-surface-area-and-mean-root-diameter-of-peach-1ho6b1fka2.pdf

M. Tagliavini

Papers

Measuring Root Surface Area and Mean Root Diameter of Peach Seedlings by Digital Image Analysis