dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion This article describes the participation rates and use patterns of professional social networks among anaesthesia researchers. We show that, despite a similar rate of use of most of these networks by women, their visibility remains lower than that of their male counterparts, particularly on the social network dedicated specifically to scientific research (ResearchGate). We also show that the proportion of women in FA/LA positions among the most frequently cited articles are similar to the rates previously described for all articles published in 2017 in two anaesthesia journals. 2 ur data support the fact that there does not seem to be any variation in the sex ratio of authors according to the scientific impact of their work (highly cited articles). However, despite a gradual increase in the number of women in anaesthesia publications, they still represent only 16% of 'prolific' authors.
In our work, the rates of Twitter use among anaesthesia researchers (between 22% and 25%) are close to those recently described (between 28% and 30%) among health policy and health services researchers. 6 However, unlike the work of Zhu and colleagues,  6  we found no differences in the number of followers between men and women. LinkedIn data also showed similar visibility for both sexes although, among prolific authors, the rate of researchers with more than 500 contacts was higher among men. On ResearchGate, men and women had similar subscription rates, similar presence of a photograph, and similar number of reported skills. Nevertheless, women's visibility on this network was much lower with visibility/reputation scores and number of followers significantly higher for men. These findings were similar in all three sub-populations of researchers studied, and these trends appeared to be more pronounced among FA.
Since the beginning of 2019, the British Journal of Anaesthesia has provided the full first names of its authors, which will make it easier to assess the rate of women publishing in this journal. Second, this work focuses on a limited population of anaesthesia researchers. Social networks evolve very quickly (subscription/unsubscription, new followers/loss of followers, etc. ), so it is essential to use data collected over a short period (here, 10 days). However, in view of the manual standardised procedure needed to detect all the accounts (which is timeconsuming), the number of researchers which could be included was limited. The choice of the denominator of our work was arbitrary and the rate of women was assessed on the basis of a specific population of authors who already have a high visibility and publish in prestigious journals.
These results are thus not necessarily generalisable to all anaesthesia researchers/journals. As it is very difficult to know at which stage of career each researcher is, it is possible that the most visible researchers are those at the end of their careers, but it was not possible to find this information reliably for all the researchers studied. Moreover, we based our selection of articles/authors on the JCR and the Web of Science Database, which do not indicate if the second author is in fact a 'second first author' with joint first authorship. It is therefore possible that we have missed researchers positing as the co-first author of a work but only appearing in second place in the list of authors. Third, our data only analyse a short and recent period, but the use of professional social media has increased in recent years and the feminisation of publications in anaesthesia is increasing.
The control of plant diseases is a major challenge to ensure global food security and sustainable agriculture. Several recent studies have proposed to improve existing procedures for early detection of plant diseases through modern automatic image recognition systems based on deep learning. In this article, we study these methods in detail, especially those based on convolutional neural networks. We first examine whether it is more relevant to fine-tune a pre-trained model on a plant identification task rather than a general object recognition task. In particular, we show, through visualization techniques, that the characteristics learned differ according to the approach adopted and that they do not necessarily focus on the part affected by the disease. Therefore, we introduce a more intuitive method that considers diseases independently of crops, and we show that it is more effective than the classic crop-disease pair approach, especially when dealing with disease involving crops that are not illustrated in the training database.
, 2017; Picon et al. , 2018) , citrus  (Iqbal et al. , 2018)  or potato  (Oppenheim and Shani, 2017). Deep Learning (DL) techniques, including Convolutional Neural Networks (CNN), have emerged as the most promising approaches given their ability to learn reliable and discriminative visual characteristics. Tranfer learning, which is used in most cases  (Slado Jevic et al. , 2016; Too et al. , 2019; Fuentes et al. , 2017; Liu et al. , 2017; Mohanty et al. , 2016; Ramcharan et al. , 2017) , is not a single technique, but a whole family of methods, comprising, among others those commonly known as fine-tuning. Unlike learning from scratch where the weights of a model are learned from scratch, the weights of a pre-trained model on a large general dataset (in terms of number of images and classes, such as ImageNet) are fine-tuned.
Transfer learning allows to build accurate models even on a specialized dataset such as those containing plant diseases where there are usually a small number of images and classes compared to ImageNet. Indeed, when used to training a CNN model based on images of diseased and healthy plant leaves captured under controlled conditions, it has been proven that fine-tuning is better than training a deep model from scratch  (Mohanty et al. , 2016). However, the ability of a deep learning model to transfer knowledge from one domain to https://doi. org/10. 1016/j. compag. 2020. 105220 Received 11 January 2019; Received in revised form 24 December 2019; Accepted 8 January 2020 another, from one task to another, is not yet well understood and performances may vary according to the used datasets for pre-training a model  (Torralba and Efros, 2011).
In particular,  (Zhou et al. , 2014)  showed that fine-tuning a CNN pre-trained on scene recognition datasets rather than the general Imagenet dataset can perform better in any urban scene recognition tasks. We can then assume that a model pretrained on a dataset of images dedicated to plant identification could provide better features for plant disease identification rather than a pretrained model on ImageNet which contains actually few visual botanical concepts. However, no previous study has reported such results. The first contribution of this article is to study the impact of transfer learning depending on whether the transfer learning is from a general domain or from the plant domain. In recent years, more and more studies address the multi-crop disease problems where data modelling covers several varieties of crops and diseases at the same time  (Mohanty et al.
, 2016; Too et al. , 2019; Slado Jevic et al. , 2016; Ferentinos, 2018). These investigations correspond to the needs of actors such as farm technicians, agricultural engineers, market gardeners or arboriculturists, who would potentially be confronted with different types of diseases on different types of crops, and where a more general approach (particularly through the use of smartphones) could provide a valuable help  (Mohanty et al. , 2016). From a technological point of view, this type of use is now possible thanks to deep learning techniques that prove to have sufficiently high recognition capabilities for wide datasets in terms of number of images, thus avoiding the need to design hand-crafted features dedicated to a specific domain. To date, two main datasets have been used to assess identification performance on multi-crop diseases: Plant Village (PV) 2   (Hughes and Salathé, 2015)  and Digipathos 3   (Barbedo et al.
, 2018). The majority of studies are based on the PV dataset  (Slado Jevic et al. , 2016; Ferentinos, 2018; Mohanty et al. , 2016; Hughes and Salathé, 2015) , which is currently the largest dataset in terms of number of images. The PV dataset is organized into target classes where each one represents a crop-disease pair, i. e. an association of one type of crop and one disease. In the PV dataset, a particular crop may be present in several classes and, conversely, visually similar diseases with the same disease common name may be present in several classes. Most of the previous studies based on CNNs follow this data organization, where the last layer dedicated to the classification contains output of crop-disease pairs  (Mohanty et al. , 2016; Too et al. , 2019; Slado Jevic et al. , 2016; Ferentinos, 2018).
Therefore, the ability of a model to transfer knowledge from one disease context to another is crucial for practical application to reduce the time and cost of data collection and retraining a deep model. We show that by using our new strategy mentioned above to identify plant diseases, we could have the significant advantage of being able to identify a disease known to the classifier without the type of crop being known. Therefore, the third contribution of this paper is to study the generalization of a deep model to identify diseases of "unseen" crops. Unseen crops in this context refer to unknown crops that have never been seen by a deep model during the training. To summarize the contributions of this paper: 1. We investigate the impact of two transfer learning strategies, one based on a pre-trained model on a plant dataset and the other using a general object dataset.
Plant Village Plant Village (PV)  (Hughes and Salathé, 2015)  is a popular dataset collected for evaluation of automatic plant disease identification systems. It contains healthy and infected leaves isolated on a uniform background. We used the initial version of the PV dataset 2 that is still publicly available as a benchmark in this study. It has 38 crop-disease pairs, with 26 crop-disease categories for 14 crop plants. Note that, the data comes with predefined training and test subsets. In our study, we use the reported configuration as the one that produces the best identification performances in the original document  (Mohanty et al. , 2016) , where 80% of the data is for the training set and the remaining 20% for the test (more precisely, 43,810 training images and 10,495 images for the test).
IPM and Bing test datasets Two complementary datasets named IPM and Bing, which were initially introduced in  (Mohanty et al. , 2016) , are used in this work to evaluate the robustness of a model for predicting plant diseases on exterior images. Unlike in PV, these test images are not limited to a controlled environment (see Fig. 1 ) and are extracted from a reliable online sources for IPM 5  and downloaded from Bing Image Search. These images were verified in  (Mohanty et al. , 2016). We were able to retrieve all the 119 images from IPM 6  mentioned in  (Mohanty et al. , 2016) , but we could only retrieve 64 of the 121 images expected for Bing 6 (missing images are no longer available online). Tables  3  and 4  show the distribution of the IPM and Bing images. Among the 38 crop-disease pairs in PV, the IPM test set allows to evaluate 19 pairs while Bing test set allows to evaluate 26 pairs.
, 2018; Mohanty et al. , 2016; Slado Jevic et al. , 2016; Ferentinos, 2018). Most of these previous works are based on models pre-trained on ImageNet  (Russakovsky et al. , 2015) , a general image database containing about 1,2 million images associated to 1000 very diverse classes (animals, vehicles, buildings, landscapes, plants, etc). However, in the case of plant diseases, one can suppose that it would be appropriate to pre-train a model on domain with a dataset containing exclusively plant images. Therefore, in this study, we compare two finetuning strategies, one directly using a model pre-trained on ImageNet and another one using PlantCLEF2015, a dataset dedicated to plant identification (H. Go¨eau, P. Bonnet, A. Joly, Lifeclef plant identification task, 2015). This dataset is related to the plant identification challenge in LifeCLEF  (Joly et al.
The hypothesis is that such intuitive modelling will engage the model to learn more independent features from the host plants. In order to train a disease classifier based on common diseases independently of crops, the images of the PV dataset are first separated into 21 classes (20 diseases and one healthy class). We randomly took a subset of images (~20%) from each healthy crop and combined them    into a single healthy class, so as to limit the amount to 2601 images in order to avoid imbalanced distributions (39,508 training images in total). During the inference, we calculate the classification result from the 21 probability outputs produced by the trained model. We call this disease modeling strategy S co which symbolizes the CNN modeling strategy based on the common name of diseases.
Generalization to unseen crops There are several hundred common plant diseases in the world that can each of them potentially infect dozens or even hundreds of different types of crops. It is difficult, if not impossible, to collect a complete set of images illustrating each crop-disease combination for building an exhaustive training set. In addition, diseases can mutate and become transmissible to new cultivated species and then it is necessary to complete regularly the training set with new crop-disease combinations. On the other hand, an approach that considers diseases independently of crops has the advantage of making a classifier more easily extensible to new crops, by allowing a model to identify a known disease in the training dataset, even if the crop hosting the diseases on a test image is unknown in the training dataset.
Training from scratch versus transfer learning All CNNs are trained by using stochastic gradient descent (SGD) optimization technique. The batch size was set to 45, and momentum set to 0. 9. The L2 weight decay with penalty multiplier was set to × 2 10 4. By employing the CNNs mentioned in Section 5. 1 and training them based on the following training schemes, we will have 9 trained models based on 3 experimental configurations: For the FTIN configuration, a model is first pre-trained with ImageNet and then fine-tuned directly on the PV dataset. For the FTPC configuration, a model is first pre-trained with ImageNet, then finetuned on the PlantCLEF2015 dataset before being fine-tuned on the PV dataset. For both configurations, all weights of the pre-trained models are fine-tuned simultaneously, with the last fully connected layer being replaced and initialized with target task classes.
Unlike what is generally observed in many other areas, the VGG16 model achieves the best performance on IPM and Bing test sets, 44. 54% and 28. 13% respectively, surpassing deeper architectures. This finding suggests that those models with deeper architectures are more likely to be overfitting for this task, probably because the PV dataset is not large or diverse enough. Pre-trained models. It is difficult to conclude which pre-training task works best for the external IPM and Bing test data mainly because the size of these two set of images is quite small. However, by comparing the overall top-1 accuracy of the combined IPM and Bing images, the  VGG16 pre-trained with the ImageNet dataset shows a better result than the one pre-trained with PlantCLEF2015, which are 44. 54% and 36. 13% respectively.
As for the GoogLeNetBN and InceptionV3, the model pre-trained on PlantCLEF2015 leads to better performance than the one pre-trained on the ImageNet dataset, unlike the VGG16. From this observation, it is unfortunately not possible to conclude on the usefulness or not of pre-training the models on a task closer to the target domain rather than the general object dataset, ImageNet. Confusions and misclassifications. By analyzing the performance of each individual class of the PV test set (Supplementary Fig. 1 ), we found that most classification errors occur in the Tomato Early blight class. A closer analysis of the confusion matrix (Supplementary Table  1 -6) shows that these errors generally result from confusion between the classes Tomato Early blight, Tomato Late blight, Tomato Septoria leaf spot and Tomato Target spot.
Fig. 4  gives some examples of misclassified images related to these confusions and illustrates how they visually share similarities in leaf appearance and disease patterns. Although the disease Tomato Early blight may have specific visual characteristics, the intra-and inter-class variability makes their learning very difficult. This factor was discussed in  (Barbedo, 2018) , and one suggested solution was to extend the training dataset to a more exhaustive and complete set, covering all the visual variability of symptoms, which is in fact very difficult to collect. Next, we found that misclassification tends to occur within similar crops, particularly in maize and potatoes, and is also misleading due to visually similar leaf appearance and disease patterns. Next, by analyzing the performance of the Bing images (Supplementary Table  7 -12), we observed that with the exception of the VGG16 model pre-trained on PlantCLEF2015 (Supplementary Table  10 ), which has the correct prediction for all the images of Potato Late blight, the rest of the model configurations show major classification errors, confusing these images with Tomato Late blight, which is actually related to the same common name of disease but on different crops.
Discussion In connection to the above findings, we hereby make a few deductions. Firstly, we found that VGG achieves a better classification performance compared to other deeper network architectures which tend to overfit on the PV dataset. We believe that the lack of diversity in the PV data set, which could make the learned model sometimes misleading when used in real field data such as IPM and Bing. Secondly, it is not possible to conclude that a model pre-trained on a task closer to the final task is the best option to solve the PV dataset, as in our study we found that pre-training on plant domains improves performance in the case of deep networks, InceptionV3 and GoogLeNetBN, but not for the simpler and shallower networks such as VGG16. However, in our experiments, the generalization performances of the models were studied on IPM and Bing, which is currently the only field test set available online with verified field truth.
It would be interesting to see if our results hold up on a larger number of images. If not, it might suggest that the size of the IPM and Bing is too small to generalize the results. Further investigation of the complementary field datasets will be required to reach a more robust conclusion on this point. We hope that this article will stimulate the curiosity of the scientific community in this field and facilitate further research on this topic related to the generalization of the deep model in cross domains to improve the identification of plant diseases. Lastly, we noticed that the features learned might not necessarily look at the most obviously infected area but the region with the most distinctive leaf characteristics, such as the venation. This is believed to be due to the fact that models are trained with the terminology of cropdisease pairs, where models trained in this way could be biased towards crop-specific patterns, especially when crop features appear to be more discriminative than diseases.
Evaluation on seen crops Table  6  shows that the S cd approach is generally better than the S co approach at predicting the PV (seen) set. The VGG16 model pre-trained with ImageNet gives the best result up to 98. 98% top-1 accuracy and 98. 20% average accuracy per class AVE. However, due to the nature of the PV data set, which seriously lacks diversity, both strategies were found achieving a relatively high accuracy (:98% for top-1 accuracy). Through the analysis of the confusion matrices (Supplementary table 19-20) of the S co and S cd approaches, we found that, compared to S cd , S co has more classification errors between early blight and late blight. In fact, this misclassification occurs mainly in tomato crops where the number of disease classes is highest in PV. From this observation, we deduce that the lack of crop information may seem to affect S co 's ability to recognize diseases with a very similar visual appearance.
However, we note that although crop characteristics that could be extracted by S cd may provide additional information to help differentiate these classes, these crop characteristics do not necessarily correspond to the characteristics of plant diseases. Next, we analyzed the performance of deep models to identify new data (IPM and Bing) from other domains, typically pictures in the field. Note that there are only two pepper crop images in IPM, and one pepper crop image in Bing, of which the number of samples is insufficient to infer the performance of deep models in recognizing the disease of an unseen crop. We hence tested the deep model using only the seen crop images. From Table  6 , we can see that S co approach in general works better than S cd on IPM and Bing. Next, using the S co approach, in order to determine which pre-training task is the most effective, we compared the top-1 accuracy of the combined IPM and Bing images, and found.
Conclusion First, this paper has examined and compared the performance of different transfer learning mechanisms based on different (1) pretraining tasks: the plant specialized domain and general object domain, and the (2) network architectures: VGG16 (16 layers), GoogLeNetBN (34 layers) and InceptionV3 (48 layers). We have experimentally proven that pre-training with plant specialized tasks could reduce the impact of overfitting for the deeper Inception-based model but the VGG16 model with ImageNet pre-training has shown a better generalization in adapting to new data. Second, the fact that the VGG16 perform better than the inceptionbased models would be due to the lack of diversity in the PV data set, which leads to a constraint in the deployment of deeper architectures. Nevertheless, with regard to the fact that ImageNet pre-training does not necessarily regularize or improve the accuracy of final target tasks, as indicated in  (He et al.
, 1811) , we suggest that the agricultural community move forward to create a broader and more diversified plant disease database, without having to use a pre-training model. In addition, for the model to better adapt to large-scale crops, a dataset of images captured under real cultivation conditions  (Barbedo, 2018; Ferentinos, 2018)  is needed, as demonstrated by  (Ferentinos, 2018). Third, by interpreting the underlying learned characteristics through the visualization of activations, we have showed that a CNN trained in this crop-disease terminology may not always focus on disease regions but crop-specific characteristics such as leaf venation or lamination to facilitate data discrimination. Therefore, in order to capture the visual symptoms of plant diseases, we demonstrated an intuitive way of leading a deep model to learn the representation of characteristics based on the common names of diseases instead of target classes of crop-disease pairs.