Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version

Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model

Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha−1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m−2 for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R2 = 0.79) and prediction accuracy (RMSE = 0.17 m2 m−2). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R2 of 0.62 and RMSE of 0.74 t ha−1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha−1 for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops

Assessing the Performance of the WOFOST Model in Simulating Jujube Fruit Tree Growth under Different Irrigation Regimes

Cropping system models are widely employed to evaluate plant water requirements and growth situations. However, these models rarely focus on growth studies of perennial fruit trees. The aim of this study was to evaluate the performance of the WOFOST (WOrld FOod STudies) model in simulating jujube fruit tree growth under different irrigation treatments. The model was calibrated on data obtained from full irrigation treatments in 2016 and 2017. The model was validated on four deficit percentages (60%, 70%, 80%, and 90%) and one full irrigation treatment from 2016 to 2018. Calibrated R2 and RMSE values of simulated versus measured soil moisture content, excluding samples on the day of irrigation and first day after irrigation, reached 0.94 and 0.005 cm3 cm−3. The model reproduced growth dynamics of the total biomass and leaf area index, with a validated R2 = 0.967 and RMSE = 0.915 t ha−1, and R2 = 0.962 and RMSE = 0.160 m2 m−2, respectively. The model also showed good global performance, with R2 = 0.86 and RMSE = 0.51 t ha−1, as well as good local agreement (R2   ≥   0.8 ) and prediction accuracy (RMSE ≤   0.62 t ha−1) for each growth season. Furthermore, 90% of full irrigation can be recommended to achieve a balance between jujube yields and water savings (average decline ratio of yield ≤ 3.8%)

Dynamics of a thin radial liquid flow

© 2016 Elsevier Ltd. All rights reserved. The present work proposes an extension of the existing analytical development on the radial spread of a liquid jet over a horizontal surface to the case of a thin radial flow. When the gap, H, between the jet nozzle and the plate is reduced the discharging area may be smaller than the inlet area leading to an increase of the main flow velocity downstream of the thin cylindrical opening. This increase of velocity, defined here as 1/α, can be related to the relative gap of the nozzle H/R with R the nozzle pipe radius. Numerical computations with a volume of fluid method were realised with for H/R ranging from 0.2 to 3 and with flow rates Q of 3 and 6 l min-1. The results of these computations allowed us to express α with respect to H/R. Taking in account the flow acceleration allowed us to extend the set of equation from the jet impacting flow to the thin cylindrical opening flow. The liquid layer thickness and the surface velocity differ with a maximum error of 4% between the flow predicted by the model and computations. Main discrepancies appear in the region close to the nozzle where the analytical model assumption of a constant velocity outside the boundary layer is not valid. However, further downstream the model and the computations are in good agreement.status: publishe