Authors:Tamás Zsom, Viktória Zsom-Muha, Lien Phuong Le Nguyen, Dávid Nagy, Géza Hitka, Petra Polgári, and László Baranyai
the excited sample and m is the sample mass in g. Full high definition (FHD) resolution digital pictures (1920 × 1080 pixels) were captured by a machinevisionsystem consisting of a Samsung WB350F digital camera mounted on a tripod, homogeneous
Authors:Tamás Zsom, Petra Polgári, Lien Phuong Le Nguyen, Géza Hitka, and Viktória Zsom-Muha
of fresh broccoli. Concerning reproducibility of the applied measurements for broccoli quality determination, the increased number of measuring points or the use of computer aided imaging methods (i.e., chlorophyll fluorescence imaging, machinevision
Authors:P. Bodor, L. Baranyai, V. Parrag, and Gy. Bisztray
Grapevine (Vitis vinifera L.) shows morphological plasticity influenced by environmental factors such as radiation and temperature. The effect of row orientation, exposition of leaves and orchard altitude on leaf morphological traits was evaluated. Grapevine cultivar ‘Furmint’ was investigated in this study with the new version of the GRA.LE.D. raster graphic software. The standard OIV (International Organization of Vine and Wine) descriptors were used with additional size parameters. High morphological variability was observed among the leaves collected from 4 different row orientations and 5 levels of expositions. Exposition levels were assigned according to the estimated total radiation collected by leaves at their position. Selected parameters also responded sensitively to changing elevation in the range of 110–289 m. According to the results, traditional leaf morphological investigations performed with machine vision systems may be recommended to reveal significant ecological factors on ampelometric traits.
Authors:S. Cubero, E. Moltó, A. Gutiérrez, N. Aleixos, O. García-Navarrete, F. Juste, and J. Blasco
The best alternative for reducing citrus production costs is mechanization. Machine vision is a reliable technology for the automatic inspection of fresh fruits and vegetables that can be adapted to harvesting machines. In these, fruits can be inspected before sending them to the packinghouse and machine vision provides important information for subsequent processing and avoids spending further resources in non-marketable fruit. The present work describes a computer vision system installed on a harvesting machine developed jointly by IVIA and a Spanish enterprise. In this machine, hand pickers directly drop the fruit as they collect it, which results in an important increase of productivity. The machine vision system is placed over rollers in order to inspect the produce, and separate those that can be directly sent to the fresh market from those that do not meet minimal quality requirements but can be used by the processing industry, based on color, size and the presence of surface damages. The system was tested under field conditions.
Authors:L. Dénes, V. Zsom-Muha, L. Baranyai, and J. Felföldi
Experiments were performed to follow the moisture loss of apple slice samples (discs of 3 cm diameter and 1 cm thickness) during the drying process. Different optical methods were tested in order to find a model for prediction of the moisture content based on non-contact measurements. Apple discs were dried in a hot air drying chamber for different periods (0–7 hours). The mass of every individual sample was measured before drying (initial mass), after drying (actual mass), and after the optical tests at the end of a 24 h drying process (final mass). Both the wet base and the dry base moisture content were calculated from the actual mass and the final mass of the samples. The optical properties of the samples of different moisture content were measured by two different optical methods. Laser induced backscattering method, applying 3 mW laser modules of different wavelengths, was used to generate the diffuse reflection pattern (halo) on the surface of samples and evaluate the halo properties with a machine vision system. Near Infrared Reflection (NIR) technique was also used to collect/measure the log(1/R) spectra of the samples in the 740–1700 nm range.PLS method with full cross-validation was used to predict the moisture content of the samples based on the backscattering data (quantitative parameters of the halo profile) and on the NIR spectra (raw and transformed log(1/R) data). Effective models (r>0.98, RPD>5) were found for prediction of the dry base moisture content of the samples based on both optical methods.