- 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 AccessEntrapped air removal by hydraulic means in gravity water systems in small diameter pipelines(MDPI, 2023-08-09) Quintana-Molina, Emilio; Prado Hernández, Jorge Víctor; Monserrat Viscarri, Joaquín; Quintana Molina, Jose RodolfoGravity water delivery systems are common around the world to transport water without the use of external energy. The systems' inadequate design and operation tend to form entrapped air bodies in downward pipeline lengths. Entrapped air generates considerable energy losses when there are no air admission-expulsion valves or due to valve failures. Air removal by hydraulic means has been modeled under various pipeline diameters, downward slopes, and air volume conditions. However, no generic entrapped air removal models exist, and the study in small diameters (≤50 mm) is limited. Most of the models reported in the literature were obtained for diameters greater than 50 mm, presenting notorious discrepancies among each other. In this research, the entrapped air removal in small diameter pipelines was studied (12.7, 15.875, 19.05, 25.4, 31.75, and 38.1 mm), highlighting the importance of the joint study of the air bubbles' removal by the turbulent action of a hydraulic jump downstream of an air body and the consequent removal of remaining entrapped air by a hydrodynamic thrust. Potential models were found for the air bubbles' removal for different pipeline diameters and downward slopes. Linear relationships were found between the dimensionless air removal parameter and the pipeline's downward slope
- 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.
- ItemOpen AccessDeterminants of grain number responding to environmental and genetic factors in two- and six-rowed barley types(Elsevier, 2023) Serrago, Román A.; García, Guillermo A.; Savin, Roxana; Miralles, Daniel J.; Slafer, Gustavo A.Context Barley is one of the most relevant crops worldwide and an essential component of agriculture in Europe in general, and in the Mediterranean region in particular. As cropping areas will hardly rise in the future, yield must be improved to enhance global crop production. Naturally, understanding how yield is affected by environmental and genetic factors in two- and six-rowed barley can help us to develop more efficient management and breeding strategies to increase current yield gains. Objective We aimed to determine the relative importance of genetic and environmental factors on numerical and physiological components of GN for two- and six-rowed barley types. Methods To generate a large and unbiased database, we compiled data of yield and its numerical and physiological components from crop-based experiments (i.e., excluding controlled-conditions experiments and/or approaches using isolated plants) reported in figures and tables in every single paper having the word "barley" in the title published over 25 years in four rigorous and prestigious international journals: Field Crop Research, European Journal of Agronomy, Crop Science and Crop and Pasture Science (formerly Australian Journal of Agricultural Research) between January 1996 and December 2021, both inclusive. Results Spike number (SN) was the most relevant numerical component explaining GN regardless of the source of variation. Regarding physiological components, it seemed that when the driving force was environmental factors, spike dry weight at flowering (SDWF) was more relevant than fruiting efficiency (FE); whilst when the differences were due to genotypic factors, clearly the FE was the component mostly responsible for the changes in GN. When the analysis was restricted to two- and six-rowed barley types, GN improvements were mainly explained by changes in SN for both two- and six-rowed barley types. However, when the physiological components were considered, the responsiveness of GN was more related to SDWF than to FE in two-rowed genotypes, while the opposite was true for the six-rowed type. Conclusions In barley, SN always explained the responses of GN better than grain number per spike (GNS), regardless the source of variation and the type of barley. Respect to the physiological components, environmental factors affected GN mainly through affecting SDWF, while genotypic factors affected GN through affecting FE. SDWF and FE were more relevant for explaining changes in GN two- and six-rowed barleys, respectively. Implications Breeders and agronomists can be aware that it will be more likely to achieve significant gains in yield through focusing more on SN than on GNS regardless of the barley type, whilst regarding the physiological components it would be more relevant to focus on SDWF in two-rowed barley, and on FE in six-rowed barley.