dataesr/scientific-paragraphs-categorization huggingface dataset:
To test their approach, they generated a dataset of five crops images acquired with a smartphone under real conditions. The approach outperformed the conventional method using only visual information with an accuracy of 98%. Some authors have tested EfficientNet on the PlantVillage dataset  [55] ; the model outperformed state-ofthe-art deep learning models achieving 99. 91% and 99. 97% in the original and augmented datasets, respectively. Likewise, in  [56] , a smaller dataset of infected tomato plant leaves images was divided into different pest attacks and plant diseases. The detection method achieved an accuracy of 95. 65% using the DensNet161 with transfer learning. For better practicability for farmers, researchers developed mobile applications for disease detection using deep learning adaptable models with mobile computing capacity and energy.
The combination of cameras mounted on a low-altitude remote sensing platform allows real-time image acquisition in precise location and with different wavelengths. For systems using these types of sensors, a large amount of data is first stored in largescale databases provided by information systems such as the geographical information system (GIS). In fact, the information system enables the visualization and analysis this data. Data collected provide information on soil and vegetation cover characteristics, such as soil organic content and soil moisture, biomass quantity, weed existence and early detection of crop stress with eventual disease stage evaluation. Traditional machine learning algorithms are used for plant disease detection using UAV images. One of the first models attempting to predict infection severity on plants from images is the Backpropagation NN (BPNN)  [62] , in which the authors extracted spectral data from remote sensing hyperspectral images of tomato plants.
Afterwards the authors rated the images according to the light blight severity based on five stages and tested the BPNN on the data extracted. The results showed that ANN with backpropagation could be used in spectral prediction for disease detection. In the same context, the authors in  [66]  attempted to detect leafroll disease using the Classification and Regression Tree. Their approach was based on spectral and spatial signatures extracted from UAV hyperspectral images of grapevine. Correspondingly, the authors in  [67]  extracted spectral bands, vegetation indices and biophysical parameters of diseased and healthy plants from UAV multispectral images. The ROC analysis was then exploited to estimate the capacity of the selected variables for disease detection. In  [68] , the authors adopted a segmentation approach based on the Simple Linear Iterative Clustering (SLIC) for soybean foliar diseases detection.
The CNN model Net-5 was tested on multiple combinations of input data and three patch sizes. The best result using combination obtained an accuracy of 95. 86%. In  [77] , the authors proposed a novel deep learning architecture for the detection of yellow rust in winter wheat at different observation times across the wheat growing. The architecture consists of multiple Inception-ResNet blocks combining the Inception and ResNet models for deep feature extraction. The model reached an overall accuracy of 85%. In another study  [78] , the authors attempted to detect disease in pinus trees using UAV images. The classification model was designed based on a combination of deep convolutional generative adversarial networks (DCGANs), and an AdaBoost classifier. The motivation behind using Adaboost classifier is to create a reinforcement learning combining the other classification models for better precision.
Satellite Imaging Even if UAV are available, the temporal aspect in the historical monitoring task may be missing. Conversely, satellites covering wider land areas offer historical images of the study area depending on satellite acquisition frequency. Plant health status can also be monitored using satellite imaging  [81]. Indeed, satellites can provide multispectral images with very high spatial resolution that can range from 0. 5 m to more than 30 m. For example, Landsat and Sentinel-2 satellite sensors provide the most widely accessible medium-to-high spatial resolution multispectral data that can be used for vegetation phenology. Table  3  details commercial satellite sensors collecting multispectral images with a spatial resolution from 0. 5 m to 30 m (Satellite Imaging Corporation (SIC)).
Nevertheless, accurate disease detection predictions require smart data processing using a smart method. Deep learning has proven its high performance for disease detection also using satellite images. In  [95] , the authors proposed a gated recurrent unit (GRU)-based model to predict development of sudden death syndrome (SDS) disease in soybean quadrats. Twelve PlanetScope satellite images were conducted in this study. The method incorporated time-series information for the classification task in different scenarios, each scenario having a different sequence size. Interestingly, the highest accuracy value was reached in the fourth scenario with the highest sequence size, which means that when enough historical images are available, precision improves. However, the study suffers from a limited dataset, which has the effect of unbalancing the development of the SDS.
This may be due to the high performance of deep learning (DL) models compared to conventional machine learning models  [60]. DL eliminates the manual feature extraction phase that can sometimes result in low prediction performance and requires less effort for feature engineering  [23]. In addition, DL models have been used to efficiently classify diseases in challenging environments with complex backgrounds and overlapping plant leaves. Conversely, traditional machine learning cannot effectively distinguish symptoms of disease with similar characteristics, nor can it take advantage of a larger number of trainings  [42]. Nevertheless, there are still considerable drawbacks to DL regarding training time that can reach weeks depending on the processor capacity of the computer used, dataset size and model complexity.
In the context of plant disease detection, datasets are not sufficiently available or are inadequate, especially when it concerns the task of early disease detection. Prior knowledge of the crop and the history of the parcels containing diseases and pests that occur is a preliminary task. To tackle this issue, researchers opt for creating their own dataset by monitoring and capturing the natural development of the infestation if it occurs  [54]  or by inoculating the fungus causing the disease in an experimental greenhouse  [50, 51]. Regarding acquisition, a hyperspectral image, for example, requires relatively expensive instruments and experts for the data collection protocol  [61]. In addition, annotation is a mandatory step for creating a new dataset. The task is time-consuming and involves agriculture experts for the annotation of different diseases since the task is not within the reach of ordinary volunteers.
In addition to the sensor data, two classical characteristics (power and median frequency) were extracted from each signal corresponding to each sensor and entered the individual channel classifier. Then, the method of weighted majority voting (WMV) was used to merge the resulting vectors, with each sensor data being weighted by a confidence measure (or weight). Hybrid fusion merges information at two or more levels. In the hybrid approach proposed in  [115] , the authors developed the merging technique of different CNN classifiers for object detection in changing environments. Three types of input modalities were used: RGB, depth and optical flow. The CifarNet architecture was designed as the single expert model, and then the outputs of each expert network model were fused with weights determined by an additional network called gating network.
This approach was called Mixture of Deep Experts (MoDE). Tensor Fusion (TFM) consists mainly of a tensor fusion layer that models unimodal, bimodal and trimodal interactions using a three-fold Cartesian product from modality integration  [132]. The architecture has been improved to lower the computational complexity; the resulting architecture is called low rank tensor fusion (LMF) model  [133]. LMF has been proposed to identify the emotions of speakers according to their verbal and non-verbal behaviors, based on visual, audio and language data. Three YouTube videos databases were used with annotation of feelings, speaker traits and emotions. The learning network for acoustic and visual modalities was represented by a two-layer neural network, and for linguistic modalities, a Long Short-Term Memory (LSTM) network was used to extract the representations.
The LMF model, compared to the tensor fusion model, performed significantly better for all datasets and measurements. Moreover, the LMF significantly reduced the computational complexity from exponential to linear. Multimodal Search Architecture Fusion (MFAS) is a generic architecture that creates a large number of possible fusion architectures, scans the neural architecture and choses the best performing architectures  [126]. The MFAS is inspired by the progressive neural architecture search (PAS)  [134]  where the search is efficiently guided for architecture sampling using temperature-based sampling  [135]. Testing three datasets, the MFAS has proven its efficacity against the state-of-the-art results on those datasets. In  [136] , the authors performed a comparison between four types of fusion (late, MoE, LFM and MiD) on image and signal modalities for automatic texture detection of objects.
Data Fusion for Yield Prediction In  [63] , the authors investigated the relationship between canopy thermal information and grain yield, using data fusion of data from different sensors. They extracted spectral (VIs), structure (vegetation fraction (VF), canopy height (CH)), thermal (normalized relative canopy temperature (NRCT)) and texture information from canopy using multi-sensors installed on a UAV. Two fusion models were used in this study, input-level feature fusion DNN (DNN-F1) and intermediate-level feature fusion DNN (DNN-F2). The DNN-F2 outperformed DNN-F1 in terms of prediction accuracy, spatial adaptability and robustness across different types of models. In  [138] , datasets of summer and winter rice yield, meteorology data and area data from 81 counties in a Chinese region were used.
Data Fusion for Crop Identification In this study  [139] , the authors exploited spatio-temporal data to segment satellite images of vegetation fields. The data used are images captured by the Gaofen 1 and 2 satellite. The authors developed a CNN 3D active architecture to extract information for the multi-temporal images. The 3D tensor is composed of the spectral, spatial and temporal characteristics of each element in each band. The convolution is done at spatial-spectral or spatial-temporal scale. In the same context, some researchers tested the feasibility of temporal CNNs (TempCNNs) for satellite images classification  [96]. To do so, they collected 46 images during one year from the Formasat-2 satellite. Three bands (NIR, red (R) and green (G)) and three VIs were used (NDVI, Normalized Difference Water Index (NDWI) and Brilliance Index (IB)).
The resulting data were then transmitted to a deep semantic segmentation module to provide a useful reference for sunflower lodging assessment and mapping. The fusion approach outperformed the no-fusion approach using the same models, and the best accuracies were achieved using the SegNet method reaching 84. 4% and 89. 8% without and with image fusion, respectively, on the test set. In another study  [141] , the authors suggested a satellite/UAV fusion technique for monitoring soybean fields using machine learning. They combined spectral canopy information (vegetation indices) extracted from Worldview-2/3 data with canopy structure features (canopy cover and height) calculated from UAV RGB images. These features were combined and fed into their ELR dual activation prediction model for plant characteristics predictions.
The approach was based on images collected in the field, in addition to contextual information (season, geographical location, temperature and humidity). The proposed model was composed of three major parts: CNN backbone for visual features extraction, ContextNet for fusion of the contextual features, and a fully connected network for fusion of all features and final predictions. The proposed approach achieved an identification accuracy of 97. 5%. Nonetheless, their method suffers from imbalanced data due to seasonal and regional difficulty with various categories of crop diseases. In another study  [143] , aiming to detect diseases in mixed and complex African landscapes, the researchers split the study into three main parts: pixel-based banana classification, object-based banana localization and disease detection.
Hence the fusion of all different important components can lead to an efficient disease detection system. The application of multimodal deep learning involves the selection of a learning architecture and algorithm. Lately, multimodal fusion has proven an inescapable potential and is increasingly used in several domains such as healthcare, sentiment analysis, human-robot interaction, human activity recognition or object detection. In the agriculture field, several deep learning fusion approaches have been proposed, such as applications in yield prediction, land monitoring, crop identification and disease detection. The most widely used type of fusion in agriculture is the fusion of multi-sensors data from aerial vehicles, fusion of multi-resolution satellites data and the fusion of satellite and UAV images.
Sex manipulation by genome editing Although the social acceptability of such approaches remains very poor  (Gremmen et al. , 2018) , the advent of CRISPR/Cas9 genome editing highlights a new opportunity to potentially generate a gender that would bear a specific marker, which would increase the feasibility to detect males from females at soon as the egg is laid, or to create all-female or all-male progeny using some of the imaging approaches outlined above. In this regard, the program EggXYT developed a gene edited breed of chickens based on the introduction of a fluorescent marker within the sexual chromosome (eggXYt). Males can be detected using fluorescence imaging and reliability is claimed to be 100%. Its state of commercial development is at the level of prototype 3. 0, which does not yet appear fast enough for high throughput hatcheries.
Acknowledgements A part of this bibliographic review was realized within the framework of a scientific and collective expertise ( ESCo INRAe ) on the quality of food of animal origin according to production and processing conditions. The bibliographic review related to ''Alternatives to the culling of one-day-old male chicks" was realized under the framework of the Poultry and Pig Low-input and Organic production systems' Welfare (PPILOW) programme. Part of this review was written in close cooperation with  Institut de Sélection Animale B. V. , a Hendrix Genetics Company.
Financial support statement JG and SRG were supported by the  European Commission  ( PPI-LOW  project, grant Number  816172 ). ICD and the  Roslin Institute  are funded by a  BBSRC   Institute strategic program  (grant  BB/ P013759/1 ). ICD is also funded by the  Foundation for Food and Agricultural Research  (grant ID  550396 ) ICD and TVB are part of a  COST action CA15224 Keel Bone Damage.
Perrier et al. (2017)  also developed a dedicated tool in ImageJ software for analysing Fasga-stained cross-sections from sorghum internodes. The dedicated script allowed quantification of the outer zone area in percentage of internode cross-section area, the percentage of sclerenchyma tissue in the outer zone, the percentage of nonlignified tissue in the central zone of the internode and the density of vascular bundles in the central zone. Apart from histochemical staining, spectral imaging techniques have been proposed to perform chemical mapping of cell wall variations in wood or plant stems. Microspectroscopy, such as Fourier transform infrared (FT-IR) or Raman microspectroscopy, is very useful to study carbohydrates or phenolic constituents (lignin or hydroxycinnamic acids)  (Gierlinger, 2018; Beć et al.
Motorised stages for positioning the camera and the samples allowed for the acquisition of large images. All elements were placed in a box to prevent outside light from entering. Homemade software developed under LabView was used for image acquisition. Sections were placed between two round lamellae for observation. Mosaic images, called large images, were acquired to observe the entire sections. The largest images corresponded to a field of view of 20 × 20 mm 2. Two sections per internode were imaged. Images of one internode were removed for M06 because air was still present after overnight rehydration. Finally, 54 large images were obtained. Examples of individual fields of view and large images can be seen in Figures  1, 2. Several tissues were observed within the stem sections: the rind, the vascular bundles and the pith parenchyma  (Esau, 1977).
Four fluorescence filter cubes corresponding to two UV excitations, namely, U1 and U2, and two visible excitations, namely, blue (BL) and green (GR), were placed inside the motorised filter wheel (Supplementary Table  1 ). The acquisition software NIS-Elements (AR 5. 02. 02) allows automatised multispectral acquisition of large images. The multispectral sequence was designed to successively acquire the four RGB images corresponding to the four fluorescence filters for a given field of view before moving to the next field of view. The order of acquisition was GR, BL, U2, and U1, with exposure times set after viewing a few samples (Supplementary Table  1 ). After all acquisitions, the fluorescence intensity was found to be much lower for the two visible filters than for the UV filters, and a multiplicative factor of 2 was applied to the RGB images of the blue and green filters.
Morphological Descriptors Measuring the Raw Morphological Descriptors Raw morphological descriptors could be directly measured from the segmented regions of interest as specified in Table  1. Areas were obtained by pixel counting; perimeters, vascular bundle length and width were obtained using the regionprops MATLAB function. Rind thickness was evaluated by granulometry using mathematical morphology transformations  (Soille, 2003; Devaux et al. , 2008; Legland et al. , 2014 Legland et al. , , 2020)) ; see "Parenchyma Cell Size" for an introduction to the method. The thickness distributions were obtained by applying opening transformations using squared structuring elements and a maximum size of 1,456 µm. The mean size of the distribution was taken as a measure of the average thickness of the rind.
Data Analysis The morphological descriptors and the autofluorescence pseudospectra were analysed based on a principal component analysis and variance analysis, followed by multiple comparisons of the estimated marginal means. Analyses were performed within the MATLAB 2019b environment (Mathworks, Natick, MA, United States) using the statistics and machine learning toolbox. Principal component analyses were applied separately to the morphological descriptors and the autofluorescence pseudospectra. The morphological descriptors were normalised to describe the variations independently of the units, and the loadings were represented as correlation circles. In the case of pseudospectra, the variables were not normalised to avoid assigning importance to the baselines of the pseudospectra, and the loadings were represented in the form of pseudospectra.
Estimating the Proportions of Tissue Cell Walls From 2D Images to Volumes of Cell Walls: Principles and Hypotheses Morphological features were extracted with the objective of examining their relationships with data such as chemical composition data or wall digestibility, which are measured on stems. In the present work, we proposed estimating the volume of tissues from 2D images considering several approximations and hypotheses. First, we considered that the internode under the ear was representative of the stem  (Méchin et al. , 1998). The internode was considered as a cylinder, and the density value of the cell walls was constant regardless of the cell type. This means that the volume and mass of cell walls are proportional. With these assumptions, the relative proportions of tissues in the internode can be estimated from their relative areas in the internode sections.
Introduction The development in a modern language (C++ objectoriented programming) of the CONRAD code started more than ten years ago at CEA-Cadarache. It was first devoted to nuclear reactions model calculations in the resonance range with data assimilation procedures based on Bayesian inference (such as SAMMY  [1]  and REFIT  [2] ). At that time an emphasis was made on experimental time-of-flight simulation and the related experimental conditions description with appropriate corrections  [3, 4]. Furthermore, a common framework to treat all uncertainties involved in the nuclear data evaluation process: experimental uncertainties (statistical and systematic) as well as model parameter uncertainties was originally proposed. Between these first developments and the present code status, additional features were added to CONRAD such as new theoretical models, new experimental descriptions as well as new Bayesian analysis features.
General principles CONRAD is a code devoted to the evaluation of nuclear data as well as theoretical model developments. Evaluation consists in making the synthesis between * e-mail: cyrille. de-saint-jean@cea. fr experimental and theoretical nuclear physics knowledge to propose numbers (referred as nuclear data) that will be used by applications (nuclear energy applications, medical applications, etc. ). CONRAD is thus organized in several major modules which are theoretical modules (theoretical nuclear physics), experimental modules (experiment description and related simulation capabilities), interface modules (input/output) and analysis modules (mainly Bayesian inference). The main idea driving CONRAD developments is to use as much as possible an objectoriented description of physical phenomena to allow a generic interface to analysis modules (derivative calculation, Bayesian inference.
For example, in Figure  1 , the method getGradMap() must return the derivatives of the implemented model with respect to a list of its internal parameters. It is not necessary for the Bayesian module of CONRAD to know exactly the physical meaning of the parameters: only mathematical derivatives are used when the Bayesian inference is called through a minimization requiring derivatives algorithm. Several models were implemented in CONRAD which allow other nuclear data type evaluation than cross section with a common philosophy. It means that all the mathematical methods developed for Bayesian studies (see Sect. 2. 3) can be used for any model that respects the imposed interface. In the past few years, fission yields models  [5, 6] , prompt fission neutron spectrum and multiplicities based on Madland-Nix model (see Sect.