Examinando por Autor "Kemper, Guillermo"

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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.
Differentiating nutritional and water statuses in Hass avocado plantations through a temporal analysis of vegetation indices computed from aerial RGB images
(Elsevier, 2023-09-22) Salazar Reque, Itamar; Arteaga, Daniel; Mendoza, Fabiola; Rojas Meza, María Elena; Soto Jeri, Jonell; Huaman, Samuel; Kemper, Guillermo
Maximizing crop production efficiently and sustainably through plant health monitoring is key for global food security. Monitoring large areas with remote sensing technologies such as unmanned aerial vehicles (UAVs) with sensors deals with time and money issues; however, the usage of advanced sensors such as hyperspectral, multispectral and thermal cameras limit their usage among all the stakeholders. In this study we explore different vegetation indices (VIs) extracted from aerial RGB images acquired in different flights to differentiate the nutritional and water statuses of Hass avocado plantations. We used an image processing workflow consisting of image selection through a convolutional neural network (CNN) model, tree crown segmentation, color correction and feature extraction to automate the computation of VIs from RGB images. To compare the performance of VIs in the differentiation of nutritional and water statuses, we proposed a comparison metric called Mean Distance between Vegetation Indices (MDVI), analyzed the evolution of the extracted features, and studied their relationships with gold standard Normalized Difference Vegetation Index (NDVI) measurements. Since the extracted features from each group vary from flight to flight due to multiple factors such as the light intensity of each season and the phenological stage of the plant, the proposed comparison metric leverages the differences between the features extracted from each group, thus reducing these temporal effects. We found that Modified Green Red Vegetation Index (MGRVI) allows a better differentiation of nutritional and water statuses. Furthermore, the correlation coefficients of this VI in the three statuses and NDVI for nitrogen group range between 0.63 and 0.85, indicating a positive strong relationship. The results of this work show that MGRVI has a potential to be used as a correlation variable in studies that only use RGB sensors in order to monitor the nutritional and water status of crops.