Examinando por Materia "Image processing"

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An algorithm oriented to the classification of quinoa grains by color from digital images
(Springer Nature, 2019-05-31) Quispe, Moisés; Arroyo, José; Kemper, Guillermo; Soto Jeri, Jonell
The present work proposes an image processing algorithm oriented to identify the coloration of the quinoa grains that make up the different samples obtained from the production of a crop field. The objective is to perform quality control of production based on the statistics of grain coloration, which is currently done manually based on subjective visual perception. This generates results that totally depend on the abilities and the particular criteria of each observer, generating considerable errors in the identification of the colors and tonalities. The problem is further complicated by the nonexistence, at present, of a pattern or standard of coloration of quinoa grains that specifically defines a referential color map. In this sense, through this work, an algorithm is proposed oriented to classify the grains of the acquired samples by their color via digital images and provide corresponding statistics for the quality control of the production. The algorithm uses the color models RGB, HSV and YCbCr, thresholding, segmentation by binary masks, erosion, connectivity, labeling and sequential classification based on 8 colors established by agronomists. The obtained results showed a performance of the proposed algorithm of 91.25% in relation to the average success rate.
Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.)
(MDPI, 2020-01-14) Cucho Padin, Gonzalo; Rinza, Javier; Ninanya, Johan; Loayza, Hildo; Quiroz, Roberto; Ramirez, David A.
Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found (𝑟𝑃𝑒𝑎𝑟𝑠𝑜𝑛 = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha−1), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m3 ha−1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.