12 páginas.-- 10 figuras.-- 3 tablas.-- 43 referenciasCharacterization of the spatio-temporal variability of tree water status is a prerequisite to conducting precise irrigation management in fruit tree orchards. This study assessed the suitability of a crop water stress index (CWSI) derived from high-resolution aerial thermal imagery for estimating tree water status variability in super high density (SHD) olive orchards. The experiment was conducted at a commercial SHD olive orchard near Seville (southwestern Spain), with drip irrigated trees under three irrigation treatments (four plots per treatment in a randomized block design): a full irrigation treatment to replace the crop water needs (ETc) and two regulated deficit irrigation treatments to replace ca. 45% of ETc. Meteorological variables, soil moisture content, leaf water potential, stem water potential and leaf gas exchange measurements were performed along the irrigation season. Infrared temperature sensors (IRTs) installed approximately 1 m above the canopies were used to derive the required Non-Water-Stressed Baselines (NWSBs) for CWSI calculation. NWSBs were not common during the growing season, although the seasonal effect could be partly explained with solar angle variations. A thermal camera installed on a mini Remotely Piloted Aircraft System (RPAS) allowed for the recording of high-resolution thermal images on 5 representative dates during the irrigation season. The CWSI values derived from aerial thermal imagery were sensitive to the imposed variations in tree water status within the SHD olive orchard. Among the recorded variables, maximum stomatal conductance showed the tightest correlation with CWSI. We concluded that high-resolution thermal imagery captured from a mini RPAS is a suitable tool for defining tree water status variability within SHD olive orchardsThis work was funded by the Spanish Ministry of Economy and Competitiveness (research project AGL2012-34544, predoctoral contract grant BES-2013-065380) and by the Junta de Andalucía (research project P12-AGR-1227).Peer reviewe

Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using off-the-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R² value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R² value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.