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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.
  • Item
    Embargo
    Enhanced detoxification via Cyt-P450 governs cross-tolerance to ALS-inhibiting herbicides in weed species of Centaurea
    (Elsevier, 2023) Palma-Bautista, Candelario; Vázquez-García, José G.; Portugal, Joao de; Bastida, Fernando; Alcántara-de la Cruz, Ricardo; Osuna-Ruiz, Maria D.; Torra Farré, Joel; Prado, Rafael
    Centaurea is a genus of winter weeds with a similar life cycle and competitive traits, which occurs in small-grains production fields in the central-southern of the Iberian Peninsula. However, most of herbicides recommended for weed management in wheat show poor control of Centaurea species. This study summarizes the biology, herbicide tolerance to acetolactate synthase (ALS) inhibitors, and recommended chemical alternatives for the control of Centaurea species. Four species (C. cyanus L., C. diluta Aiton, C. melitensis L. and C. pullata L. subsp. baetica Talavera), taxonomically characterized, were found as the main important broadleaf weeds in small-grains production fields of the Iberian Peninsula. These species showed innate tolerance to tribenuron-methyl (TM), showing LD50 values (mortality of 50% of a population) higher than the field dose of TM (20 g ai ha−1). The order of tolerance was C. diluta (LD50 = 702 g ha−1) ≫ C. pullata (LD50 = 180 g ha−1) ≫ C. cyanus (LD50 = 65 g ha−1) > C. melitensis (LD50 = 32 g ha−1). Centaurea cyanus and C. melitensis presented higher foliar retention (150–180 μL herbicide solution), absorption (14–28%) and subsequent translocation (7–12%) of TM with respect to the other two species. Centaurea spp. plants were able to metabolize 14C-TM into non-toxic forms (hydroxylated OH-metsulfuron-methyl and conjugated-metsulfuron-methyl), with cytochrome P450 (Cyt-P450) monooxygenases being responsible for herbicide detoxification. Centaurea cyanus and C. mellitensis metabolized up to 25% of TM, while C. diluta and C. pullata metabolized more than 50% of the herbicide. Centaurea species showed 80–100% survival when treated with of florasulam, imazamox and/or metsulfuron-methyl, i.e., these weeds present cross-tolerance to ALS inhibitors. In contrast, auxin mimics herbicides (2,4-D, clopyralid, dicamba, fluroxypir and MCPA) efficiently controlled the four Centaurea species. In addition, the mixture of ALS-inhibitors and auxin mimics also proved to be an interesting alternative for the control of Centaurea. These results show that plants of the genus Centaurea found in the winter cereal fields of the Iberian Peninsula have an innate tolerance to TM and cross-resistance to other ALS-inhibiting herbicides, governed by reduced absorption and translocation, but mainly by the metabolization of the herbicide via Cyt-P450.
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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
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    Open Access
    Amodal segmentation for on-tree apple fruit size es timation with RGB-D images
    (2023) Gené Mola, Jordi; Gregorio López, Eduard; Ferrer Ferrer , Mar; Blok, Pieter M.; Hemming, Jochen; Morros Rubió, Josep Ramon; Rosell Polo, Joan Ramon; Vilaplana Besler, Verónica; Ruiz Hidalgo, Javier
    The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. CONCLUSIONS The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. Future works should evaluate the performance of the method with commercial RGB-D sensors, which would facilitate data collection.
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    Open Access
    Uncertainty analysis of a LiDAR-based MTLS point cloud using a high-resolution ground-truth
    (2023) Lavaquiol Colell, Bernat; Llorens Calveras, Jordi; Sanz Cortiella, Ricardo; Arnó Satorra, Jaume; Escolà i Agustí, Alexandre
    The study of plant geometry is crucial to design specific management by providing the optimal quantities of nutrients, fertilizers, pesticides and irrigation rates. Before the advent of the first 3D characterization systems, it was very laborious to obtain accurate commercial scale 3D crop data. Nowadays, there are sensing systems which allow 3D canopy characterization to be performed in a relatively simple and fast way. LiDAR (light detection and ranging) sensors have been widely used in agriculture. When 3D scanning techniques are used, it is essential to be aware of the total measurement error. One of the limitations when using real data is the absence of ground-truth (GT) to compare the obtained measurements . In a previous research [1], validated a high-resolution 3D point cloud on an actual defoliated tree obtained from RGB images and stereo-photogrammetry techniques. This accurate 3D point cloud can be used as digital ground-truth (DGT) to validate 3D LiDAR point. The accuracy of the scanning system includes the errors committed by the sensor, the positioning system (GNSS), the data acquisition set up, the point cloud generation algorithms and the georeferentiation of the DGT.