dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, at most of the right-bank tributary stations, the free run discharge peak is higher than the observed discharge peak (see Fig. 5 , panels 7-12). However, the seasonal cycle is well reproduced for all these stations. These results illustrate the necessity of conducting the DA experiments. Then, Fig. 6  displays the global performances of the free run. For each ENVISAT virtual station (see Fig. 6a ) and each in situ station (see Fig. 6b ), the RMSEn (defined in Eq. 19) between the simulated and observed discharges is calculated and its value is indicated by a colour at the location of the station over the basin. The results are similar between EN-VISAT and gauge discharges, confirming good concordance between the two discharge datasets. RMSEn shows important deviations in basin heads on most of the tributaries as well as at the confluence between right-bank tributaries and the mainstream.
From Table  4 , we can see that the RMSE between the free run discharge and both the ENVISAT and gauge discharges is reduced for all experiments, showing that the data assimilation platform is working correctly. The SE1-diag experiment gives the worst results when compared to both the EN-VISAT discharge and the gauge discharge. Then, compared to ENVISAT discharges, SE1-local gives the best results by reducing the global RMSEn by more than 56 % (49 % for SE1-direct), while SE1-direct presents slightly better global statistics than SE1-local when compared to gauge discharges (RMSEn is reduced by 16. 5 % for SE1-direct and by 15. 25 % for SE1-local). Overall, the global statistics are more reduced when compared to ENVISAT discharges than to gauge discharges. This is due to the fact that gauge discharges are not directly assimilated, unlike ENVISAT discharges.
Overall, analysis discharge is quite close to observed discharges (ENVISAT and in situ). However, despite the use of the localization, the analysis discharge keeps presenting a quite chaotic behaviour: more     particularly at Sao Paulo de Olivenca (Fig. 14 , panel 1), Manacapuru (Fig. 14 , panel 2) and during high flow seasons along right-bank tributaries (Fig. 14 , panels 7 to 12). This shows the limit of assimilating 35-day repeat period ENVISAT observations. If no data are missing at a given VS, it means that there will be, at most, 11 available observations during 1 year. Moreover, in a state estimation context, only the model output state is corrected and not the model parametrization or, in our set-up, forcings. Therefore, if the model is not constrained by direct or neighbouring observations, it naturally goes back to free run discharge.
Conclusions and perspectives This study presents, over the Amazon basin, the assimilation of a satellite-derived discharge product into a large-scale hydrological model to correct its state variables. The remotely sensed discharge data are derived from the ENVISAT nadir altimeter and are assimilated into the ISBA-CTRIP model using an ensemble Kalman filter. Five experiments were carried out over the year 2009. For all experiments, the assimilations were able to reduce the modelling errors compared to both observed and gauge discharges. The first experiments tested different definition of the background error covariance matrices, where the influence of a given observation is either reduced to the only observed cell (SE1-diag), or limited to a few close cells on the hydrological network (SE1-local), or not limited and can potentially impact the entire basin (SE1-direct).
Results showed that correcting river discharge gives the best global results over the entire basin, as the link between the observed and corrected variables is the most straightforward. Therefore, the final experiment (SE3-local-long) uses the SE3-local configuration over the whole ENVISAT observation period (from September 2002 to 2010) and confirms the possibility of using such low-resolution remotely sensed data in a large-scale model. These experiments offer several perspectives. First, the localization treatment could be improved by combining the three tested approaches according to the cell's position on the river: discharge correction for cells along the mainstream should be impacted by all upriver observations, while correction for cells on tributaries should be impacted only by close observations along the same sub-catchment.
Moreover, the size of the area of influence for a given observations could also vary in time according to the season (high flow/low flow). With ulterior developments of the localization method, new challenges may appear such as the risk of imbalance, already studied in the field atmospheric DA (e. g. Greybush et al. , 2011). The analysis of imbalance may need to be considered in future works. A main limitation of assimilating ENVISAT data is their low repeat period (one observation every 35 days, at best). Indeed, corrected discharges often present strong sudden variations between unobserved and observed dates, as the model goes back to its free run when it is not constrained by an observation. However, there are other satellite altimetry missions with different repeat periods, for example JASON-2 (10-day repeat period from June 2008 to October 2016), JASON-3 (10-day repeat period, launched in January 2016), Sentinel-3A (27-day repeat period, launched in February 2016) or Sentinel-3B (27-day repeat period, which should be launched in 2018).
Also, the incoming SWOT (Surface Water and Ocean Topography, launch scheduled for 2021) wide-swath altimetry mission will provide a remotely sensed discharge product. SWOT will have a 21-day repeat period, with an almost global spatial coverage thanks to its two 50 km swaths. All these data could be combined with ENVISAT data (during the overlapping period) within the assimilation scheme to have a denser network of observation over the study domain, to get a better estimate of discharge (similar to a reanalysis) over a multi-decadal time frame  (Tourian et al. , 2017). To improve these DA results, several aspects could be investigated. For example, one could study whether a more realistic ensemble method generation could be helpful. In the present study, only the model initial condition and the precipitation forcing are perturbed to generate the background forecast ensemble.
More uncertainties in this ensemble could be added by also perturbing CTRIP parameters and/or ISBA outputs. Another DA aspect to look into is the potential use of a smoothing data assimilation algorithm, such as the ensemble Kalman smoother  (Evensen and Leeuwen, 2000). A smoother could help to have less "variability" in the corrected discharge. Finally, the assimilation scheme presented in this study could be applied to other river basins in the world, as ISBA-CTRIP is a global LSM. However, more work is needed to apply the DA platform at a global scale. Code and data availability. The CTRIP code is open source and is available as a part of the surface modelling platform called SURFEX, which can be downloaded at http://www. cnrm-game-meteo. fr/surfex/. SURFEX is updated approximately every 3 to 6 months and the CTRIP version presented in this paper is from SURFEX version 7.
3. If more frequent updates are needed, please follow the procedure to obtain a SVN or Git account in order to access real-time modifications of the code (see the instructions at the previous link). The ISBA-CTRIP model is coupled to the DA codes via the OpenPalm coupler available at http://www. cerfacs. fr/globc/PALM_WEB/. To get the DA routines coupled to ISBA-CTRIP with OpenPalm, please directly contact C. Emery (charlotte. emery@jpl. nasa. gov) or S. Biancamaria (sylvain. biancamaria@legos. obs-mip. fr). To obtain the GSWP3 forcings, please refer to the following url: http://search. diasjp. net/en/dataset/GSWP3_EXP1_Forcing (https://doi. org/10. 20783/DIAS. 501). The ENVISAT-based discharge data are available by contacting A. Paris (aparis@cls. fr). Finally, the in situ discharges used to validate the results are available on the ANA website (http://hidroweb.
Acknowledgements. This work has been performed using HPC resources from  CALMIP  (grant  2016-P1408 ). The GSWP3 team is acknowledged for letting the authors use their different forcing fields. This work was supported by the  CNES , through a grant from the  Terre-Océan-Surfaces Continentales-Atmosphère (TOSCA) committee  affiliated with the project entitled "Towards an improved understanding of the global hydrological cycle using SWOT measurements". The  European Space Agency (ESA)  is also thanked for providing to the scientific community observations from the RA2 altimeter embarked on ENVISAT. Charlotte Marie Emery was supported by a  CNES/région Midi-Pyrénées  PhD grant. This work was done as a private venture and not in the author's capacity as an employee of the  Jet Propulsion Laboratory, California Institute of Technology.
Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images.
Weeds detection and characterization represent one of the major challenges of the precision agriculture. In the literature several methods of detection of weeds are proposed with different acquisition system. Compared to robot or satellite acquisitions, drones have been considered more efficient since they allow a fast acquisition of the field with very high spatial resolution and low cost  [5, 6]. Despite the important advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem. In recent years, deep learning techniques have shown a dramatic improvement for many computer vision tasks, but still not widely used in agriculture domain. Indeed recent development showed the importance of these techniques for weeds detection  [7, 8]. However, the huge quantities of the data required in the learning phase, have accentuated the problem of the manual annotation of these datasets.
The same problem rises in agriculture data, where labeling plants in a field image is very time consuming. So far, very little attention have been payed to the unsupervised annotation of the data to train the deep learning models, particularly for agriculture. In this paper, we propose a new fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training set labeling for weeds detection on UAV images. This method is performed in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase interline weeds are used to constitute our training dataset. Finally, we performed CNNs on this database to build a model able to detect the crop and weeds in the images. This paper is divided into five parts.
The use of wavelets proved to be efficient for the detection of weeds even at a stage of growth of beet greater than 6 leaves. [22]  evaluate weeds detection with support vector machine and artificial neural networks in four species of common weeds in sugar beet fields using shape features. In  [23]  a semi-automatic Object-Based Image Analysis (OBIA) procedure have been developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. With all these articles, we can notice that the selected features change in general from one type of culture to another or from one type of data to another. Recently, convolutional neural networks have emerged as a powerful approach for computer vision tasks. CNNs  [24]  progressed mostly through the success of this method in ImageNet Large Scale Vision Recognition Challenge 2012 (ILSCVR12) and the creation of AlexNet network in 2012 which showed that a large, deep convolutional neural network is capable of achieving record-breaking results on a highly challenging dataset using purely supervised training  [25].
For a good optimization of deep learning models it is necessary to have a certain amount of labeled data. But as mentioned before creating large agricultural datasets with pixel-level annotations is an extremely time consuming task. Little attempts have been made to develop fully automatic system for training and identification of weeds in agricultural fields. In a Recent work,  [29]  suggest the use of synthetic training datasets. However, this technique requires a precise modeling in terms of texture, 3D models and light conditions. [30]  an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera mounted on a pole kept manually.
The spatial approach (Hough Transform) is used to detect crop rows and to build a training dataset. SVM is applied to the spectral information to make classification. This method assumes weeds and crops have different spectral information and that is not always the case in agricultural fields. The success of this kind of method lies on better features selection which involves a human analysis according to the agriculture field. To the best of our knowledge there is no work for weeds detection on UAV images using automatic labeling of training images and deep learning.
Unsupervised training data labeling The unsupervised training dataset annotation is based on the detected lines obtained in previous section. According to hypothesis that the lines detected are mainly at the center of the crop rows (Figure  3 ) we performed a mask to delimit the crop rows. Hence, the vegetation overlapped by the mask correspond to the crop. This mask is obtained from the intersection of superpixels formed by the simple linear iterative clustering (SLIC) algorithm  [33]  and the detected lines. SLIC is chosen since it is their spatial proximity. A higher value of compactness makes superpixels more regularly shaped. A lower value makes superpixels adhere to boundaries better, making them irregularly shaped. Since here the goal is to create a mask around the detected crop lines able to delimit the crop rows we have chosen a compactness of 20 because we found it was less sensitive to the variation of color caused by the effect of light and shadow.
Crop/Weeds classification using Convolutional Neural Networks CNNs are a part of Deep learning approach, they showed impressive performances in many computer vision tasks  [34]. CNNs are made up of two types of layers, the convolutional layers which  However, according to the size of data we used the ResNet with 18 layers (ResNet18) described in  [35]  because it has shown a better result than AlexNet and VGG13  [36]  in the ImageNet challenge. Due to abundant categories and significant number of images in ImageNet, studies revealed the performance of transferability of networks trained with ImageNet dataset. Thus we performed fine tuning to train the networks in our data. Fine-tuning means that we start with the learned features on the ImageNet dataset, we truncate the last layer (softmax layer) of the pre-trained network and replace it with new softmax layer that are relevant to our own problem.
This heterogeneity may be a difference of soil moisture, presence of straw, etc. In order to make our models robust to the background, we mixed samples with background and no background. Samples without background were obtained by applying ExG followed by Otsu's thresholding on previously created samples (see Figure  7 ). We evaluated the performance of our method by comparing models created by data labeled in supervised and unsupervised way. The supervised training dataset were labeled by human experts. A mask is performed manually on the pixels of weeds and crops. Figure  8  presents weeds manually delineated by an expert in red. The supervised data collected were also unbalanced so we have carried out the same data augmentation procedure performed on the unsupervised data. The total number of samples is shown in the Table  1.
Evaluation of ResNet results After the creation of both weed and crop classes, 80% of samples were selected randomly for the training, and the remaining ones were used for validation. The Tables  1  and 2  present the training and validation data performed on each field. For finetuning we tested different values of the learning rate. The initial learning rate is set to 0. 01 and updated every 200 epochs. The update is done by dividing the learning rate by factor of 10. Figure  9  shows the evolution of the loss function during training for supervised and unsupervised datasets for spinach and bean fields. From these figures we can notice that the validation loss curves decrease during about the first 80 epochs before to increase and to converge (behavior close to overfitting phenomenon). This overfitting phenomenon is less emphasized in the supervised labeled data of bean.
In Figure  12  and Figure  13  we have noticed that the color, Haralick and geometric features give the best results. In the field of spinach, the strong presence of thistle with leaves color different from that of spinach at a certain level of growth explain the effectiveness of the color features. In the bean field, the color features were less effective than the texture features  (Haralick)  in both dataset since we have young potatoes shoots from the previous sowing among the weeds and they have a color almost similar to that of the bean plants.
Preprints By Using SVM, while the features are combined, the improvement is less than 2% for data labeled in a supervised manner and about 10% for unsupervised data in the spinach field. In the bean field the same remark applies to the data collected in a supervised manner, for unsupervised data collected no improvement has been found. Another remark that can be made is that from one type of data to another the best features are not the same. We also noticed that the selected features are not suitable to detect the weeds present in the bean field. With the RF, the feature selection procedure only increased performance by about 1% for both spinach dataset. In the bean field an improvement of about 1% have been observed on data labeled in unsupervised way and about 5% for data labeled in supervised way.
Conclusion In this paper, we proposed a novel fully automatic learning method using Convolutional Neuronal Networks CNNs) with unsupervised training dataset collection for weeds detection from UAV images taken in bean and spinach fields. The results obtained have shown close performance to the supervised data labeling ones. The Area Under Curve (AUC) differences are 1. 5% in the spinach field and 6% in the bean field. Supervised labeling is an expensive task for human experts and according to the gaps of accuracies between the supervised and the unsupervised labeling, our method can be a better choice in the detection of weeds, especially when the crop rows are spaced. The proposed method is interesting in terms of flexibility and adaptivity, since the models can be easily trained in new dataset.
Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 5 September 2018 doi:10. 20944/preprints201809. 0088. v1 Figure 1. Flowchart of the proposed method The main advantage of such technique is that it is unsupervised and does not depend on the training data. Indeed, based on this hypothesis we detect first the crop rows, then inter-line vegetation is used to constitute our training database which is categorized into two classes crop and weed. Thereafter, we performed CNNs on this database to build a model able to detect the crop and weeds in the images. The flowchart (Figure 1) depicts the main steps of the proposed method. Next sections describes in details each step.
Acknowledgements : Figures 5 and6 were obtained in cooperation with SOVIVINS (Martillac, France). We are grateful to the staff of the  Vignobles Bernard Magrez  and its research and development unit for help with data acquisition for YAN, δ 13 C and vigor maps (Figures 2, 5 and6). Analyses from Pauillac soil (Table 1a) were reproduced with kind permission from  Olivier Trégoat  and analyses from  Mazis-Chambertin  soil (Table 1b) with kind permission from ADAMA (Flavignerot, France).
To assess the past evolution of diseases risk, a database composed by yearly number of applications of phytosanitary treatments for powdery and downy mildew diseases was built. This information was collected from 400 grapevine growers originating from 5 sub-regions of Burgundy. The data refer to yearly average number of treatments during the 1995-2014 period. Pesticide applications was related to climate by means of multiple linear regression models between the average number of treatments for powdery and downy mildews control and monthly temperature and monthly rainfall indices from April to July. Models providing the lowest error (estimated trough leave-one-out cross-validation) were selected for each of the 5 Burgundy wine sub-regions. According to each region, mildews yearly treatments number were significantly related to monthly climate data.
Spraying mildew database Disease pressure is a fuzzy concept used by various grape growing stakeholders which assessment metrics are yet to be developed. Disease pressure can be roughly estimated by using different indicators such as the number of applied treatments, experts (e. g. consultants) or grapegrowers opinions, and disease intensity levels observed in the vineyard. In this study, we used the number of applications done by grapegrowers each year as a bulk estimate of the disease intensity, as previously performed by  [4]. We collected this information from 400 grapevine growers originating from all 5 sub regions of Burgundy. In this paper, we focus on the data of the "Côte de Beaune" collected by the Agricultural Regional Chamber of Burgundy which refer to yearly average number of powdery and downy mildew treatments during the 1995-2014 period.
Observation Monthly climate data was obtained for each pixel of a 12 km resolution grid elaborated though interpolation of daily climate data from Meteo France weather stations. Interpolation was performed by regression kriging as described by  [12]. Variables included are total precipitation, minimum and maximum air temperature at daily time step. These data were used to elaborate as an empirical model in order to estimate mildew's pressure in Burgundy, for the 1995 to 2014 period. For each Burgundy wine sub region, climate data was extracted from several 12 km grid cells where grapevine is currently grown. These correspond to five, two, ten, four and ten grid cells for "Côte de Beaune", "Côte Chalonnaise", "Chablisien", "Côte de Nuits" and "Mâconnais" respectively.
Simulation In the case of future climate simulations, CMIP5 climate projections were collected at from ESGF (https://esgfnode. llnl. gov/search/cmip5/). Data from the following eigtheen GCM (General Circulation Models) are used: ACCESS, BCC, CanESM2, CCSM4, CNRM, CSIRO, GFDLM2G, GFDLM2M, INMCM, IPSLCM5A, IPSLCM5B, MIROC, MIROC5, MPILR, MPIMR, MRI, Nor. Here only the RCP8. 5 (Representative Concentration Pathways) simulation, corresponding to very high greenhouse gas emissions  [13]  are used. The simulated climate data time period cover 120 years from 1980 to 2100. Data are statistically downscaled and debiased at a 12km grid resolution using the robust quantile mapping method  [14]. Note that the quantile mapping is applied at monthly time step.