- ItemOpen AccessRelationship between yield and tree growth in almond as influenced by nitrogen nutrition(Elsevier, 2023) Sandonís Pozo, Leire; Martínez Casasnovas, José Antonio; Llorens Calveras, Jordi; Escolà i Agustí, Alexandre; Arnó Satorra, Jaume; Pascual Roca, MiquelUnderstanding the relationship between nitrogen (N), tree growth and yield, can maximize productivity and sustainability. This study analyzed the effect of N on canopy development and its relation with yield in a super-intensive almond orchard in Spain over two seasons. The treatments included 50 kg N ha−1, 100 kg N ha−1, 150 kg N ha−1, and 100 kg N ha−1 applied between 3.1 and 7.7 Growth Stages, and their combinations with a nitrification inhibitor, DMPSA. The canopy was measured using LiDAR technology after pruning in spring and before harvest. Differences were found in canopy parameters comparing early N (Nstop) against N applied along the season. The treatments N50, N100 and N150 resulted in higher cross sections and widths, less porosity and higher yield, fruit set and hull weights. In contrast, Nstop gave higher porosity and higher flower density. DMPSA produced more homogeneous canopies and improved N use efficiency, combined with N100 or N150. These findings provide evidence to support the management of N in super-intensive orchards.
- ItemOpen AccessSimultaneous fruit detection and size estimation using multitask deep neural networks(Elsevier, 2023) Ferrer Ferrer, Mar; Ruiz Hidalgo, Javier; Gregorio López, Eduard; Vilaplana Besler, Verónica; Morros Rubió, Josep Ramon; Gené Mola, JordiThe measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits.
- ItemOpen AccessImpact of carob (Ceratonia siliqua L.) pulp inclusion and warm season on gastrointestinal morphological parameters, immune-redox defences and coccidiosis in concentrate-fed light lambs(Elsevier, 2023-08-11) Pelegrin-Valls, Jonathan; Álvarez Rodríguez, Javier; Martín-Alonso, María José; Aquilué, Beatriz; Serrano, BeatrizThis study aimed to evaluate the effects of dietary carob (Ceratonia siliqua L.) pulp and warm season on gastrointestinal morphological parameters, immune-redox defences and coccidiosis in concentrate-fed light lambs. Weaned lambs were assigned to one of three concentrate-based diets: C0 (without carob pulp), C15 (150 g/kg of carob pulp) and C30 (300 g/kg of carob pulp) from 40 to 80 days of age in two consecutive cold and warm batches. Blood samples were collected at Day 80 to determine the metabolic status. Rectal faeces were sampled at Days 50, 65 and 80 to determine consistency and oocyst count per gram. Inclusion of carob pulp in lamb diets did not affect lamb growth but reduced coccidia oocyst excretion, improved faecal consistency and gastrointestinal morphological parameters, enhancing the ruminal thickness of the papilla living strata and reducing the darkness of the epithelium colour. Moreover, carob condensed tannins in the lambs' diet enhanced the expression of antioxidant SOD2 in rumen, while down-regulated NRF2, SOD1, CAT and PPARG in ileum. There was no interaction between the treatments and season in the evaluated variables. Lambs from the warm season exhibited reduced growth performance, altered ruminal epithelium, lower circulating iron levels, increased protein concentrations and higher coccidiosis susceptibility. In addition, regulatory immune and antioxidant mechanisms to counterbalance reactive oxygen species production in gastrointestinal tissues were evident. Dietary inclusion of carob pulp (150 and 300 g/kg) in lamb diets improved gastrointestinal health and homeostasis but did not ameliorate the deleterious effects of warm season.
- ItemOpen AccessMobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters - Part 2: Comparison for different crops and training systems(Elsevier, 2023-07-26) Torres-Sánchez, Jorge; Escolà i Agustí, Alexandre; de Castro, Ana I.; López-Granados, Francisca; Rosell Polo, Joan Ramon; Sebé Feixas, Francesc; Jiménez-Brenes, Francisco M.; Sanz Cortiella, Ricardo; Gregorio López, Eduard; Peña, José M.The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.
- ItemOpen AccessMobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters - Part 1: Methodology and comparison in vineyards(Elsevier, 2023-08-01) Escolà i Agustí, Alexandre; Peña, José M.; López-Granados, Francisca; Rosell Polo, Joan Ramon; de Castro, Ana I.; Gregorio López, Eduard; Jiménez-Brenes, Francisco M.; Sanz Cortiella, Ricardo; Sebé Feixas, Francesc; Llorens Calveras, Jordi; Torres-Sánchez, JorgeCharacterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions.