Articles publicats (Grup de Recerca en AgròTICa i Agricultura de Precisió)
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- ItemOpen AccessProyecto PAgFRUIT - Avances en la aplicabilidad de tecnologías en la agricultura de precisión en fruticultura(Editorial Tècnica Quatrebcn, 2023) Martínez Casasnovas, José Antonio; Escolà i Agustí, Alexandre
- ItemOpen AccessSoluciones de agricultura de precisión para el control de malas hierbas en maíz(Eumedia, 2023) Montull, José M.; Llenes, J. M.; Martínez Casasnovas, José Antonio; Escolà i Agustí, AlexandreDurante los últimos años se vienen desarrollando diferentes trabajos con el objetivo de integrar nuevas tecnologías en el control de las malas hierbas del maíz que nos permitan mantener los rendimientos con una menor cantidad de insumos, haciendo que su producción sea más eficiente y sostenible. En este artículo se detallan algunos de los avances surgidos de dichos trabajos.
- ItemOpen AccessDrip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot(MDPI, 2023) Arnó Satorra, Jaume; Uribeetxebarria Alonso de Armiño, Asier; Llorens Calveras, Jordi; Escolà i Agustí, Alexandre; Rosell Polo, Joan Ramon; Gregorio López, Eduard; Martínez Casasnovas, José AntonioTo optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.
- ItemOpen AccessAssessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples(Elsevier, 2023-10-07) Miranda, Juan Carlos; Arnó Satorra, Jaume; Gené Mola, Jordi; Lordan Sanahuja, Jaume; Asin Jones, Luis; Gregorio López, EduardData acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.
- ItemOpen AccessAKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards(Elsevier, 2023-10-06) Miranda, Juan Carlos; Arnó Satorra, Jaume; Gené Mola, Jordi; Fountas, Spyros; Gregorio López, EduardAKFruitYield is a modular software that allows orchard data from RGB-D Azure Kinect cameras to be processed for fruit size and fruit yield estimation. Specifically, two modules have been developed: i) AK_SW_BENCHMARKER that makes it possible to apply different sizing algorithms and allometric yield prediction models to manually labelled color and depth tree images; and ii) AK_VIDEO_ANALYSER that analyses videos on which to automatically detect apples, estimate their size and predict yield at the plot or per hectare scale using the appropriate algorithms. Both modules have easy-to-use graphical interfaces and provide reports that can subsequently be used by other analysis tools.