Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions.

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Introduction Lateral dislocation of the patella is a common injury in active adolescents and young adults. Patients who are ultimately managed surgically have a significantly lower risk of recurrent dislocation. However, determining the optimal surgical treatment remains a challenge, with patients sometimes undergoing multiple surgeries prior to successful stabilization. The aim of this study is to computationally evaluate patients that have undergone multiple surgeries to correct for recurrent lateral patellar dislocation and predict their clinical outcome. Methods Our patient cohort consisted of 16 patients with patella dislocation. Patient-specific imaging were used to create three-dimensional (3D) finite element (FE) models of the knee joint and evaluate patellofemoral (PF) stability at multiple time points pre- and post-surgery for each patient. We applied these models to predict the clinical success or failure of each surgery. Specifically, the FE model simulated a knee extension activity while a tibia external torsion, a recognized cause of patellofemoral pain and instability, was applied to assess PF stability. A healthy control group of 12 participants was also included to assess the ability of the model to identify successful outcomes. In addition, five anatomic factors of risk were measured, and statistical analysis was performed to establish if significant differences exist among pre-surgery, post-surgery and healthy control groups. Lastly, a logistic regression model was implemented, trained with anatomic values, and used to classify subjects into likelihood of dislocation categories in order to differentiate between successful and unsuccessful surgical outcomes. Feature scaling and feature combination (namely, principal component analysis (PCA)) was applied to improve the predictive performance of the regression model. Results Of 12 control participants, 12 pre-surgery subjects (8 patients after an initial unsuccessful MRPLR and 4 without any), and 9 post-surgery subjects (5 after a successful trochleoplasty and 4 patients after MPFLR), the FE model correctly predicted 29 out of 33 surgery outcomes (87.9% accuracy). Post-surgery simulations predicted patellofemoral stability metrics similar to the healthy control group. Particularly, post-trochleoplasty subjects were associated with an increased ability to provide constraint force on the patella lateral facet, and a lower involvement of the medial patellofemoral ligament, particularly close to full extension. A one-way ANOVA showed that four out of five anatomic factors were significantly different between the pre-surgery and the control group, and three of them also between the pre- and post- surgery group, suggesting that the surgery was able to restore a physiological condition. Lastly, logistic regression classification performance demonstrated 72.2% and 78.9% accuracy before and after PCA, respectively. Conclusion The overall aim of this study is to provide surgeons with a useful and validated computational tool that can predict the likelihood of patellar dislocation and differentiate, prior to clinical intervention, between a successful versus unsuccessful surgery, to determine the optimal treatment pathways for individual patients. Preliminary results are promising, but an improvement of the model and a larger clinical dataset are necessary to improve accuracy and comprehensively validate model performance.

<p>Optical and thermal sensors installed on Unmanned Aircraft Systems (UAS) can be considered a technological innovation for precision farming. The visible and thermal regions of the electromagnetic (EM) spectrum provide useful information to assess the quality of crop growth and monitor plant water status. Accurate measurements of plant water status with high-resolution thermal images associated with high-efficiency irrigation systems can be a suitable solution to improve energy and water saving.</p> <p>The objective of this work was to estimate and compare the Crop Water Stress Index (CWSI) obtained in a citrus orchard irrigated with two different irrigation systems, by using a UAS equipped with a thermal camera.</p> <p>The experiment was carried out in a commercial citrus orchard located in the Northwest of Sicily, Italy, during the irrigation season of 2022. Optical and thermal high-resolution images were acquired at noon on August 23 and 25, and September 2 over two plots, the first of which was irrigated with a subsurface drip irrigation (SDI) and the second with a micro-sprinkler (MSI). Hourly crop reference evapotranspiration, ETo, and Vapour Pressure Deficit (VPD) were calculated by using the weather variables measured by a standard weather station installed in the field, while the plant water status was monitored at an hourly time scale, through three microtensiometers (FloraPulse, Davis, CA) embedded into the woody tissue of trees considered representative of the two irrigation systems. For each thermal image, characterized by a thermal spatial resolution of 15 cm,  soil pixels were initially removed; then, the dry and wet reference temperatures, T<sub>dry</sub> and T<sub>wet</sub>, were estimated as the 0.5 and 99.5 percentiles of the canopy temperature. The values of CWSI were finally calculated based on the maximum T<sub>dry</sub> and minimum T<sub>wet</sub> obtained in the two plots during the examined days.</p> <p>Vapor pressure deficit and crop reference evapotranspiration resulted in quite similar values in the three days, with hourly VPD and ETo at noon ranging between 1.49 and 1.65 kPa, and between 0.50 and 0.62 mm, respectively. Irrigation heights provided in the examined period resulted equal to 65 mm in a single application in the MSI plot and 48 mm, equally distributed in eight irrigation events, in the SDI plot. In the latter plot, the values of daily stem water potential ranged between -0.5 and -1.1 MPa during the entire period with values of the corresponding CWSI between 0.22 and 0.28; on the other hand, in the plot irrigated with the MSI system the values tended to decline to a daily range between -1.1 and -1.3 MPa as a consequence of the soil drying between consecutive waterings with values of CWSI ranging between 0.30 and 0.34. The analysis showed that both plots were characterized by low water stress levels. However, despite the lower irrigation volume supplied by the SDI system, the values of CWSI resulted always lower than those obtained under the MSI system, confirming the potential of the SDI system to improve water use efficiency. </p>