Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting ...patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Background
Fibrotic non-alcoholic steatohepatitis (NASH), i.e., the concomitant presence of active inflammation and fibrosis, represents a milestone in the natural history of NAFLD and a critical ...time point in its progression. The purpose of this study was to analyze the diagnostic accuracy of the non-invasive Fibrotic NASH Index (FNI) in individuals with obesity undergoing bariatric surgery.
Methods
This is a cross-sectional study, enrolling individuals who underwent bariatric surgery with liver biopsy at a tertiary university hospital. FNI was calculated, and a cutoff value was determined. Its diagnostic accuracy was then calculated through comparison with the gold standard test for this analysis (histopathological examination).
Results
Of 128 participants, 83.6% were female, and the average age was 39.8 ± 8.7 years. The mean BMI was 38.7 ± 5.7 kg/m
2
. NAFLD was histologically confirmed in 76.6%, of which 81.6% had NASH. Histologically confirmed fibrotic NASH was observed in 22.7% of the general study population, 29.6% of individuals with NAFLD, and 36.3% of those with NASH. The mean FNI was 0.18 ± 0.19. An optimal cutoff point of 0.21 was determined, with an overall accuracy of 90.1%, an 82.8% sensitivity, a 90.8% specificity, a 72.6% positive predictive value, and a 94.7% negative predictive value.
Conclusions
FNI provided adequate accuracy in detecting and ruling out fibrotic NASH. Considering the importance of fibrotic NASH within the natural history of NAFLD progression and the fact that this marker uses simple variables, it may be of great importance in high-risk populations, and its external validation and use should be encouraged.
Graphical Abstract
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
For wireless networks beyond 5G, directivity and reconfigurability of antennas are highly relevant. Therefore, we propose a linear antenna array based on photodiodes operating at 300 GHz, and an ...optical phased array based on polymer waveguides to orchestrate the antennas. Due to its low thermal conductivity and high thermo-optical coefficient, the polymer chip enables highly efficient and crosstalk-free phase shifting. With these, we demonstrate purely photonic-controlled beam steering across 20°. Compared to a single emitter, the 3-dB beam width is reduced by 8.5° to 22.5° and the output power is >10 dB higher. Employing Snell's law for coupling into air, we can precisely predict the radiation patterns.
•Machine learning to model the spectral response of maize under insect-attack.•Most contributing regions are in the Red (600–700 nm) and NIR ranges (900–1350 nm).•A ranking is combined with a ...self-organizing method to highlight spectral regions.
Accurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
We present a fiber coupled transceiver head for terahertz (THz) time-domain reflection measurements. The monolithically integrated transceiver chip is based on iron (Fe) doped In
Ga
As (InGaAs:Fe) ...grown by molecular beam epitaxy. Due to its ultrashort electron lifetime and high mobility, InGaAs:Fe is very well suited as both THz emitter and receiver. A record THz bandwidth of 6.5 THz and a peak dynamic range of up to 75 dB are achieved. In addition, we present THz imaging in reflection geometry with a spatial resolution as good as 130 µm. Hence, this THz transceiver is a promising device for industrial THz sensing applications.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The
Spodoptera frugiperda
(i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically ...do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall armyworm attacks using machine learning algorithms, culminating in a theoretical model creation based on the band simulation process. A controlled experiment was conducted to collect hyperspectral radiance measurements from health and damage cotton plants over eight days. A hand-held spectroradiometer operating from 350 to 2500 nm was used. Several algorithms were evaluated, and a ranking approach was adopted to identify the most contributive wavelengths for detecting the damage. The Self-Organizing Map method was applied to organize the spectral wavelengths into groups, favoring the theoretical model creation for two sensors: OLI (Landsat-8) and MSI (Sentinel-2). It was found that the Random Forest algorithm produced the most suitable model, and the last day of analysis was better to separate healthy and damaged plants (F-measure: 0.912). The best spectral regions range from the red to near-infrared (650 to 1350 nm) and the shortwave infrared (1570 to 1640 nm). The theoretical model returned accurate results using both sensors (OLI, F-Measure = 0.865, and MSI, F-Measure = 0.886). In conclusion, the proposed framework contributes to accurately identifying cotton plants under the
Spodoptera frugiperda
attack for both hyperspectral and multispectral scales.
Urban forests are an important part of any city, given that they provide several environmental benefits, such as improving urban drainage, climate regulation, public health, biodiversity, and others. ...However, tree detection in cities is challenging, given the irregular shape, size, occlusion, and complexity of urban areas. With the advance of environmental technologies, deep learning segmentation mapping methods can map urban forests accurately. We applied a region-based CNN object instance segmentation algorithm for the semantic segmentation of tree canopies in urban environments based on aerial RGB imagery. To the best of our knowledge, no study investigated the performance of deep learning-based methods for segmentation tasks inside the Cerrado biome, specifically for urban tree segmentation. Five state-of-the-art architectures were evaluated, namely: Fully Convolutional Network; U-Net; SegNet; Dynamic Dilated Convolution Network and DeepLabV3+. The experimental analysis showed the effectiveness of these methods reporting results such as pixel accuracy of 96,35%, an average accuracy of 91.25%, F1-score of 91.40%, Kappa of 82.80% and IoU of 73.89%. We also determined the inference time needed per area, and the deep learning methods investigated after the training proved to be suitable to solve this task, providing fast and effective solutions with inference time varying from 0.042 to 0.153 minutes per hectare. We conclude that the semantic segmentation of trees inside urban environments is highly achievable with deep neural networks. This information could be of high importance to decision-making and may contribute to the management of urban systems. It should be also important to mention that the dataset used in this work is available on our website.
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Evidence on the effect of one-anastomosis gastric bypass (OAGB) on renal function is limited.BACKGROUNDEvidence on the effect of one-anastomosis gastric bypass (OAGB) on renal function is limited.To ...compare the evolution of estimated renal function observed 1 year after OAGB and Roux-en-Y gastric bypass (RYGB) in individuals with obesity.OBJECTIVETo compare the evolution of estimated renal function observed 1 year after OAGB and Roux-en-Y gastric bypass (RYGB) in individuals with obesity.Observational, analytical, and retrospective cohort study. Tertiary-level university hospital.DESIGN AND SETTINGObservational, analytical, and retrospective cohort study. Tertiary-level university hospital.This study used a prospectively collected database of individuals who consecutively underwent bariatric surgery. Renal function was assessed by calculating the estimated glomerular filtration rate (eGFR), according to the Chronic Kidney Disease Epidemiology Collaboration. The one-year variation in the eGFR was compared between the procedures.METHODSThis study used a prospectively collected database of individuals who consecutively underwent bariatric surgery. Renal function was assessed by calculating the estimated glomerular filtration rate (eGFR), according to the Chronic Kidney Disease Epidemiology Collaboration. The one-year variation in the eGFR was compared between the procedures.No significant differences in age, sex, obesity-associated conditions, or body mass index were observed among individuals who underwent either OAGB or RYGB. OAGB led to a significantly higher percentage of total (P = 0.007) and excess weight loss (P = 0.026). Both OAGB and RYGB led to significantly higher values of eGFR (103.9 ± 22 versus 116.1 ± 13.3; P = 0.007, and 102.4 ± 19 versus 113.2 ± 13.3; P < 0.001, respectively). The one-year variation in eGFR was 11 ± 16.2% after OAGB and 16.7 ± 26.3% after RYGB (P = 0.3). Younger age and lower baseline eGFR were independently associated with greater postoperative improvement in renal function (P < 0.001).RESULTSNo significant differences in age, sex, obesity-associated conditions, or body mass index were observed among individuals who underwent either OAGB or RYGB. OAGB led to a significantly higher percentage of total (P = 0.007) and excess weight loss (P = 0.026). Both OAGB and RYGB led to significantly higher values of eGFR (103.9 ± 22 versus 116.1 ± 13.3; P = 0.007, and 102.4 ± 19 versus 113.2 ± 13.3; P < 0.001, respectively). The one-year variation in eGFR was 11 ± 16.2% after OAGB and 16.7 ± 26.3% after RYGB (P = 0.3). Younger age and lower baseline eGFR were independently associated with greater postoperative improvement in renal function (P < 0.001).Compared with RYGB, OAGB led to an equivalent improvement in renal function 1 year after the procedure, along with greater weight loss.CONCLUSIONCompared with RYGB, OAGB led to an equivalent improvement in renal function 1 year after the procedure, along with greater weight loss.
We report for the first time the successful wavelength stabilization of two hybrid integrated InP/Polymer DBR lasers through optical injection. The two InP/Polymer DBR lasers are integrated into a ...photonic integrated circuit, providing an ideal source for millimeter and Terahertz wave generation by optical heterodyne technique. These lasers offer the widest tuning range of the carrier wave demonstrated to date up into the Terahertz range, about 20 nm (2.5 THz) on a single photonic integrated circuit. We demonstrate the application of this source to generate a carrier wave at 330 GHz to establish a wireless data transmission link at a data rate up to 18 Gbit/s. Using a coherent detection scheme we increase the sensitivity by more than 10 dB over direct detection.
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This study aimed to evaluate the influence of botanical composition calibration on the accuracy of undisturbed sward height and comparative yield method (CYM) techniques for herbage mass estimation ...in tropical heterogeneous pastures. Two studies were conducted using two grazing systems based on heterogeneous pastures. Herbage mass was estimated using CYM and undisturbed sward height techniques within quadrats (SHQ) or at a set of standard points in the paddocks (SHP). SHQ had a higher adjusted R
2
when calibrated with the Urochloa brizantha cv. Marandú (U. brizantha) proportion compared to the simple SHQ model (0.75 vs 0.68), while RSE was lower (0.18 vs 0.21). The R
2
increased (0.63 to 0.68), while both residual means and RSE decreased (−2.30 to -0.05 and 0.22 to 0.20, respectively) when SHP was calibrated with U. brizantha. It also resulted in a reduction of mean squared prediction error (MSPE) to CYM, SHQ, and SHP, respectively. The CYM allows for higher accuracy of herbage mass estimation in tropical heterogeneous pastures than undisturbed sward height, irrespective of botanical calibration. However, the botanical composition calibration had positive effects on the models from undisturbed sward height, and the calibration with U. brizantha increased the accuracy of herbage mass prediction.
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