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    Open Access
    Video-Based Fruit Detection and Tracking for Apple Counting and Mapping
    (IEEE, 2023) Gené Mola, Jordi; Felip Pomés, Marc; Net-Barnés, Francesc; Morros Rubió, Josep Ramon; Miranda, Juan Carlos; Arnó Satorra, Jaume; Asin Jones, Luis; Lordan Sanahuja, Jaume; Ruiz-Hidalgo, Javier; Gregorio López, Eduard
    Automatic fruit counting systems have garnered interest from farmers and agronomists to monitor fruit production, predict yields in advance, and identify production variability across orchards. However, accurately counting fruits poses challenges, particularly due to occlusions. This study proposes a multi-view sensing approach using continuous motion videos captured by a camera moved along the row of trees, followed by fruit detection in all video frames and application of Multi-Object Tracking (MOT) algorithms to prevent double-counting. Three tracking methods, namely SORT, DeepSORT, and ByteTrack, are compared for fruit counting using the YOLOv5x object detector. The methodology is applied to map fruit production in an experimental apple orchard at two different dates: four weeks and one week before harvest. The results demonstrate that ByteTrack (MOTA=0.682; IDF1=0.837; HOTA=0.689) outperforms SORT and DeepSORT, indicating its superior tracking performance. Computational efficiency analysis reveals similar processing times between SORT and ByteTrack (about 15 ms), while DeepSORT requires significantly more processing time per image (128 ms). Fruit counting evaluation shows reasonably accurate yield predictions on both dates, with reduced errors and improved performance closer to the harvest date (MAPE=7.47 %; R2=0.70). The system proves effective in estimating orchard fruit production using computer vision technology, offering valuable insights for yield forecasting. These findings contribute to optimizing fruit production and supporting precision agriculture practices. The code and the dataset have been made publicly available and a video visualization of results is accessible at http://www.grap.udl.cat/en/publications/video_fruit_counting.
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    Open Access
    Investigating the effects of moderate wine consumption on Alzheimer's disease in Aß- and Tau-pathology mice models
    (EDP Sciences, 2023) Montero-Atalaya, Marta; Pérez-Matute, Patricia; Íñiguez, M.; Recio-Fernández, Emma; Motilva, María-José; Yuste, Silvia; León-Espinosa, G.; Herreras, Óscar; Bartolomé, Begoña; Moreno-Arribas, M. Victoria
    La enfermedad de Alzheimer (EA) es la forma más común de demencia y tiene una elevada morbilidad y mortalidad. La EA se caracteriza principalmente por la presencia de dos estructuras aberrantes en el cerebro de los pacientes, placas seniles formadas por péptido-β-amiloide (Aβ) y ovillos neurofibrilares cuyo principal componente es la proteína tau fosforilada. Aunque actualmente no se conoce bien la etiopatogenia, cada vez son más los estudios que demuestran un efecto causal del microbioma intestinal sobre la EA y las funciones cognitivas, a través del "eje microbiota intestino-cerebro". Las evidencias científicas sugieren un posible efecto protector de los polifenoles del vino frente a los trastornos neurodegenerativos aunque se desconocen los mecanismos y, hasta el momento, los estudios para evaluar de forma exhaustiva el efecto del vino sobre la etiopatogenia de la EA son muy escasos. El objetivo principal de la línea de investigación que enmarca este trabajo es entender cómo la dieta, y especialmente los polifenoles presentes en los alimentos vegetales, y otros factores del estilo de vida interactúan con el microbioma oral e intestinal, en relación con la salud digestiva y el deterioro cognitivo. Para ello, se está llevando a cabo una aproximación experimental que tiene como finalidad evaluar el posible efecto protector de los polifenoles del vino, mediante la suplementación de la dieta en dos modelos murinos de la EA (patología Aß y Tau), y, por otro lado, se está profundizando en el estudio de los mecanismos de protección mediante la evaluación de los efectos del ácido protocatéquico sobre la actividad eléctrica del cerebro.
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    Open Access
    Novel approach based on artificial intelligence to evaluate individual wine intake
    (EDP Sciences, 2023) Cobo Cano, Miriam; Regaño de la Guía, Edgard; Heredia, Ignacio; Aguilar, Fernando; Lloret Iglesias, Lara; García Díaz, Daniel; Yuste, Silvia; Recio-Fernández, Emma; Pérez-Matute, Patricia; Motilva, María-José; Moreno-Arribas, M. Victoria; Bartolomé, Begoña
    This study arises from the need to propose new methodologies to quantify wine consumption more precisely in order to use subsequently this information in observational food-health studies and dietary intervention studies. It has been developed an algorithm based on a “deep learning” method to determine wine volume from a single-view image, and it has been validated through a consumer study developed via a web application. The new model demonstrated satisfactory performance not only in a “daily lifelike” images dataset but also in “real” images (obtained from the consumer study), with a mean absolute error (MAE) of 10 and 26 mL, respectively. In relation to the data reported by the participants in the consumer study (n=38), average red wine volume in a glass was 114±33 mL, without being affected by factors such as gender, time of consumption, type of wine or type of glass. Therefore, the deep learning system together with the web application developed in this study constitute a diet monitoring tool of substantial value in the accurate assessment of daily wine intake, as well as in the habits of its consumption, with relevant applications in observational studies.
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    Open Access
    Yield prediction using mobile terrestrial laser scanning
    (2019) Gené Mola, Jordi; Gregorio López, Eduard; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Rosell Polo, Joan Ramon
    Yield prediction provides valuable information to plan the harvest campaign, fruit storage and sales. Traditionally, yield estimation has been carried out by manual counting of randomly selected samples, without addressing spatial variability within the orchard. To obtain a precise estimation it is necessary to sample a relatively large number of trees, which is unfeasible with manual counting. To solve this issue, this work proposes the use of a Mobile Terrestrial Laser Scanner (MTLS) for fruit detection and yield prediction. Experimental test were carried out in a commercial Fuji apple orchard. The row of threes was scanned from the two sides (east and west). The measurement equipment consisted of an MTLS comprised of a LiDAR sensor, and a real-time kinematics global navigation satellite system (RTK-GNSS) connected to a rugged laptop. The LiDAR sensor used was a Puck VLP-16 (Velodyne LIDAR Inc., San José, CA, USA), which provides a 3D point cloud with calibrated reflectance values of the measured scene. The fruit detection algorithm implemented in this work is divided into four steps: (1) Reflectance thresholding, which delete those points presenting a reflectance lower than 60%; (2) Connected Points Clustering using DBSCAN; (3) Fruit separation, which uses a support vector machine (SVM) to predict the number of fruits that contains each cluster; (4) False positive removal, also based on a trained SVM. From detections obtained with this algorithm, the yield was predicted using a linear model (obtained with training data) that relates the number of detections and the actual number of fruits Three different trials were evaluated: east (E) side scanning, west (W) side scanning and merging data from both scanned sides (E+W). As it was expected, fruit detection results showed lower detection rates when only scanning from one tree side, presenting detection rates of 38.3% and 48.5% for east and west sides, respectively. However, the detection rate increased up to 75.8% when using E+W data. Similarly, yield prediction results showed higher errors when using data from only one tree side, obtaining a RMSE of 15.2% and 15.3% (east and west, respectively). The prediction improved significantly when using data from both tree sides (E+W), presenting a RMSE of 5.4%. From these results it is concluded that MTLS has potential in yield prediction in fruit orchards. Although fruit detection rates are moderately successful, the system was able to predict the actual number of fruits with low estimation errors. Only using data from one tree side increases the prediction error, but it has de advantage of reducing a 50% the scanning time, which may be interesting depending on the application and the interest of the farmer. Future works will extend this study to other fruit varieties.
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    Open Access
    Low-cost terrestrial photogrammetry for orchard sidewards 3D reconstruction
    (2023) Martínez Casasnovas, José Antonio; Rosell Tarragó, Miquel; Rosell Polo, Joan Ramon; Sanz Cortiella, Ricardo; Gregorio López, Eduard; Gené Mola, Jordi; Arnó Satorra, Jaume; Plata Moreno, José Manuel; Escolà i Agustí, Alexandre