dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Dukono volcano in Indonesia regularly has small ash producing eruptions and degassing  (Carn et al. , 2017; Bani et al. , 2018; Engwell et al. , 2021) , but the record within the GVP database is a single eruption that has been continuing since 1933, and would be counted as a single eruption within our analysis. Therefore, an important consideration when interpreting our frequency-magnitude results is they are a probability of an eruption period beginning in a year, rather than the probability of any individual explosive eruption. We found that the prior models for volcanoes assigned a "small cone" analogue class within the A1 classification system have relatively very large uncertainty compared to other analogue classes. Whilst we did not see the same uncertainty with the Distributed Cones and Fields class within the A2 classification system, there are far fewer volcanoes within the A2 system (n = 341 vs.
Specifically, for small and large temporal resolutions, is smaller when downscaling with CDA, however, for intermediate temporal resolutions, DDA downscaled fields result in a smaller. Finally, our results illustrate that varies quadratically with the temporal resolution of the observational data in the case of CDA as opposed to the more sensitive DDA error where was observed to vary cubically with the temporal resolution of the observations. The distribution of the downscaled fields were also examined by applying a statistical hypothesis test. Specifically, after reaching the plateau, the downscaled fields pass the Kolmogorov-Smirnov test at almost all tested locations in the domain, suggesting that they are normally distributed. The precision of the estimated skill scores was also assessed following standard bootstrapping.
Acknowledgements D. M. S. , R. E. , L. H. , L. S. G. , T. J. , T. S. , X. L. , and P. O. were supported by the  EU H2020   APPLICATE  project ( GA727862 ). The Met Office contribution was also supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra  and by the  UK-China Research and Innovation Partnership Fund  through the  Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. J. A. S was supported by  NERC  grants  NE/P006760/1 ,  NE/R005125/1  and  NE/V005855/1. G. M and Y. P. were supported by the  US Department of Energy , grant number  DE-SC0019407. L. S. G was also supported by the  Research council of Norway INES  project ( 270061 ), and the  Norwegian e-infrastructure for Research and Education  ( UNINETT Sigma2 ) through projects  NN2345K ,  NS2345K  and  NS9034K.
J. G. -S. and P. O. were supported by the  Spanish Ramón y Cajal' programme  ( RYC-2016-21181 ,  RYC-2016-22772 ). B. H. was jointly funded by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (Grant No. XDA19070404 ) and the  National Natural Science Foundation of China  (Grant Nos. 42030602 ,  91837101 ). G. G. was supported by the  EU H2020 Blue-Action  ( GA727852 ) project and uses the HPC resources of TGCC under the allocations  2018-R0040110492  and  2019-A0060107732  made by  GENCI. J. S. acknowledges the project  L4  of the Collaborative Research Centre TRR 181 Energy Transfers in Atmosphere and Ocean funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Project  274762653.
Data availability PAMIP datasets analysed during the current study are available from the CMIP data archive https://esgf-node. llnl. gov/projects/cmip6/. Data for HadGEM3-LL are available from https://zenodo. org/record/5127891, and data for OpenIFS are available from https://cera-www. dkrz. de/WDCC/ui/cerasearch datasets DKRZ_LTA_995_ds00003 to DKRZ_LTA_995_ds00008 inclusive. Reanalyses EP fluxes are available from the Centre for Environmental Data Analysis https://catalogue. ceda. ac. uk/uuid/dafbd838e4cc4c68a 5ccdd90690ea57f. HadISST1. 1 sea ice observations are available from https://www. metoffice. gov. uk/hadobs/hadisst/. ERA5 reanalysis data are available from https:// www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5.
Statistical and computational analysis Prey preference was examined by comparing the abundance of a cell type in the food environment relative to the abundance of that cell type ingested. Parametric statistical tests were used (ANOVA and Pearson's). Results were considered significant at the 0. 05 level. When assumptions of normality, linearity and variance homogeneity were not maintained, non-parametric tests were used (Spearman's rho, r). Averages shown are mean average followed by standard deviation. Statistical analysis and data visualisation were done using the R programming environment (R Core Team, 2021) using the packages ggplot2 (Wickham, 2016) and viridis  (Garnier, 2018).
Classical algorithms (support vector machine, random drill, k-nearest neighbor) of machine learning have been applied in many studies to extract image features such as color, shape, or texture  (Liakos, Busato, Moshou, Pearson & Bochtis, 2018). These algorithms require designing features of objects manually and are highly dependent on the data acquisition methods, preprocessing, and feature extraction performance. With the upgrading of computational performance and large volume datasets, the algorithms of deep learning have been effective in many areas. Due to their expressive capabilities on the data, they have been able to avoid the disadvantages of traditional extraction methods. This allows the machine to perform complex processing on big data, providing predictions with promising results.
2 ). The first step is a keyword search of conference papers or journal articles, book chapters, and other media to obtain bibliographic data. This step was performed in the IEEE Xplore digital library and the Association for Computing Machinery (ACM) digital library, as well as in scientific indexing services such as Web of Science (WoS), Elsevier Scopus, or Google Scholar. WoS seems to be widely used because of its multidisciplinary nature. Indeed, databases can be very generalist like Google Scholar or very specialized like IEEE Xplorer. On the other hand, Scopus, developed by Elsevier, clearly does not meet the criteria of open access. Hence, in this work, we focus on WoS, which also offers advanced search features useful for selecting significant sets of articles that can be considered as an interesting basis for building our bibliometric analysis.
It should be noted that the data collected was processed to eliminate spuriously (such as abbreviations) information that would have hindered the subsequent stages of our study. The analysis and visualization phase of the search queries were carried out using the Bibliometrix tool  (Aria & Cuccurullo, 2017)  which is easy to use with intuitive web interfaces. Bibliometrix is an R package for conducting quantitative research in scientometrics (e. g. , the science of measuring and analyzing science) and bibliometrics. It permits to import of bibliographic data from several sources such as Scopus or WoS. At the same time, it offers an evaluation system based on co-citation as well as the measurement of scientific collaboration.
Data collection The objective of this study is to map the scientific structure of deep learning research in the agriculture sector through bibliometric analysis, visualization, and network analysis. For this purpose, as the query keywords, we used the following query on 1 June 2022 in the WoS database (Web of Science Group, 2022): ("deep learning") AND ("agriculture*" OR "farming") The question was intentionally formulated in a large way to cover more research topics. It considers the years of publication, title, abstract, and author/indexed keywords of the articles. The bibliographic data was downloaded as a BibTeX file. In a filtering process, we selected Englishlanguage documents up to 2022. As a result, 1406 documents are identified and analyzed by Biliometrix tool. Through citation analysis, we explored the relationship between authors and papers to provide an understanding of the underlying issues in precision agriculture.
Top manuscripts by citations Document citation analysis studies the influence of articles in the literature. In doing so, we performed a citation analysis to identify the most influential studies on the application of deep learning in agriculture and recapitulated the content. The Table  4  list the top ten most influential documents in terms of the total number of citations (TC) or average citations per year (ACY). The publications by  Kamilaris and Prenafeta-Boldú, (2018) ;  Kussul, Lavreniuk, Skakun and Shelestov, (2017)    were identified, including the domain and problem they address, the models used, the data sources, etc. Additionally, they compared the performance of deep learning with other existing methods. Several emerging opportunities exist for strengthening the role of remote sensing in agricultural applications, according to  Weiss et al.
, (2020). As a complement to these previous efforts, this current work aims to help agriculture users interested in deep learning applications. We will provide an overview of what deep learning has to offer agriculture based on literature review tools. It represents the most cited journals, articles, or keywords. In the agriculture sector, accurate and earlier detection of fungal attacks in crops could help to develop an effective treatment technique while substantially reducing economic losses. Developments in deep neural networks have allowed researchers to improve the accuracy of object detection and recognition models by iterating over a training dataset. The authors  (Fuentes et al. , 2017)  present a solution to this problem. They proposed a deep learning-based approach to detect plant diseases and pests in tomato crops  (Fuentes et al.
Their objective is to reduce the cost of labeling (tagging) training data for the object-counting problem by creating a set of secondary datasets for training. Using a modified version of the GoogleNet or Inception and ResNet model coupling architecture, their algorithm counts the exact number of fruits or flowers. Experimental results trained on simulated data returned an average accuracy of 91% on a set of real images. To support agriculture, (Khan, Khan, Yousaf, Khurshid & Abbas, 2018;  Kussul et al. , 2017; Zhong, Hu & Zhou, 2019)  illustrate the development of techniques based on remote sensing. The authors  (Kussul et al. , 2017; Zhong et al. , 2019)  compare machine learning models to CNN for crop classification, used to delimit crop areas. Kussul et al. compare  the models as Multi-Layer Perceptron (MLP) or Random Forest with CNN for crops (wheat, corn, soybeans, etc.
, 2019; Zeiler & Fergus, 2013). When the model is used in real situations such as plant diagnosis, the reduction of classifier parameters is important for memory and computational efficiency. It is therefore essential to select features and evaluate influence factors when developing these classifiers  (Hariri, Bagheri & Davoodi, 2022). Layer visualization during feature extraction or input prediction is a first step to building architectures with fewer learning parameters and weakly supervised object localization  (Choe et al. , 2020; Zhang, Cao & Wu, 2020). Furthermore, it was demonstrated that Visions Transformers (ViT) achieve very competitive performances for vision applications like image classification, object detection, and semantic image segmentation  (Steiner et al. , 2022). Compared to convolutional neural networks, which can present inductive biases when trained on small datasets, the contributions of ViTs are interesting for researchers who wish to optimize the performance of their final model according to their computational resources.
Dataset for agricultural field Many public image datasets like ImageNet  (Krizhevsky et al. , 2012) , MS COCO  (Lin et al. , 2015) , Pascal VOC  (Everingham, Van Gool, Williams, Winn & Zisserman, 2007 , 2012)  are available to evaluate deep learning models. Using these datasets, it has been possible to evaluate the performance of the algorithms for classification, detection, localization, or object segmentation problems. Despite the sufficient size of these datasets, they are mainly composed of generic objects that cannot be directly applied to farms. To address the agricultural dataset limitation, some investigators have proposed large-scale image datasets: projects like NBAIR (NBAIR, 2022), AgriPest (R. Wang et al. , 2021) , DeepWeeds  (Olsen et al. , 2019) , PlantVillage  (Arun & Gopal, 2019)  IP102  (Wu, Zhan, Lai, Cheng & Yang, 2019) , and web repositories as plant leaf disease  (Munnangi, 2019 (Munnangi, /2022;; Rai, 2021) , cotton diseases  (Naik, 2020 (Naik, /2022)) , Insect pests  (Xie et al.
Evaluation metrics Deep learning requires a large quantity of training dataset to train neural networks. They are exploited by networks with complex architectures having a significant training parameter. Moreover, it is a very expensive task to train these networks from scratch for hardware re- sources (computational speed and storage). To address this challenge, some works consider transfer learning techniques, the combination of several training techniques to validate their approach  (Arsenovic, Karanovic, Sladojevic, Anderla & Stefanovic, 2019; C. -J. Chen et al. , 2020; Kerkech et al. , 2020). The experimental results obtained are measured using the common metrics such as Accuracy, F-score, Precision, Recall, and mean Average Precision (mAP) given by Eqs. (  2 )-8 respectively. They each indicate the percentage of misclassified instances compared to correctly classified instances.
Future of deep learning in agriculture Agriculture has undergone a remarkable development over the last decades thanks to new technologies. The resulting new practices offer farmers operational efficiency in their activities and better profitability. The computing power of machines, the use of computer vision in IoT, and deep neural networks have attracted both scientific and agribusiness communities' attention. To discover the limitations of different works, we analyzed their experiments, the structure of their networks, or their results. Some studies are based on very few datasets, while others lack information about the experimental setup (hardware, time, memory). However, the analysis results present some limitations in terms of the visualization of the influential factors in the prediction and the use of multiple classifiers.
Firstly, if multiple classifiers are included in the experiments, then it is necessary to conduct statistical tests to analyze the significance of the achieved results  (Benavoli, Corani, Demšar & Zaffalon, 2017; Demšar, 2006). Demšar et al. examined several statistical non-parametric tests, safety, and robustness and studied their suitability in machine learning to analyze significant differences in classification performance. The tests that have been recommended are the Wilcoxon signed ranks test for the comparison of two classifiers and the Friedman test with posthoc tests for the comparison of multiple classifiers on multiple datasets. The results of this test can be presented using Critical Difference (CD) plots for clarity. Secondly, when we want to estimate the robustness of a model centered on random sampling, the cross-validation technique on the training data is useful.
Conclusion Precision agriculture is a recent science that is certainly still in its young phase. Deep learning can make a decisive contribution to the analysis of agricultural data, in this case by using computer vision to enable a machine to automatically analyze and understand the visual world. This offers the opportunity to develop intelligent systems, which appear to be one of the many possible ways to tackle the economic, environmental, and social challenges in agriculture. This literature review shows a richness of work in recent years that describe the development of various systems using deep learning to solve various problems in precision agriculture. The problems addressed are mainly yielded losses caused by factors affecting crop growth. In bibliometric methodology, we explored only documents from the Web of Science database.
smart sprayer to learning to detect label with artificial plants, and 59% and 44% differentiate specific target weeds Acquisition: respectively with real plants. between weeds and was developed and Three cameras acquired tomato plants on evaluated. images simultaneously real and artificial on a resolution of data. 640 × 480 pixels (Ruigrok et al. , CNN Darknet-53 based on YOLOv3 Plant weed Development of an 2260 images of the Recall: 57%, Precision: 84% 2020) for object detectors + data detection system automated spraying training dataset split augmentation (translation, integrated into a system for potatoes into 6383 sugar beet rotation, etc. ). spraying robot. and 1709 potato annotations Acquisition: The images acquired by the camera system with a resolution of 2048 1538 pixels. (Bah et al.
, 2018) CNN based ResNet18) + feature Deep learning of Combine supervised Supervised data Spinach fields: extraction and fine-tuning. unsupervised data and unsupervised labeling: Areas Under the Curve (AUC): 94. 34% With labeling for crop training datasets. The -28,886 bean training (unsupervised data labeling) and 95. 70% learning rate of 0. 01 weed detection in developed method dataset (supervised data labeling). epochs: 200 drone images from could be a key -14,188 spinach Beanfield: bean and spinach technique for online training dataset AUC: 88. 73% (unsupervised data fields. weed detection with Unsupervised data labeling) and 94. 84% (supervised data UAV. labeling: labeling) -9616 bean training dataset -8606 spinach training dataset Acquisition: A DJI Phantom 3 Pro- drone that embeds a 36- megapixel (MP) RGB camera acquired images.
(Lottes, Behley, A combination of CNN (Fully Crop and weed An approach for Subset training dataset F1-score: 92. 4% for test n • 1 (training on Milioto & Convolutional classification agriculture robots image: Bonn2016 and testing on Stuttgart) and Stachniss, DenseNet) + Autoencoder system on sugar providing semantic -10,036 for Bonn2016 86. 6% on test n • 2 (training on Bonn2016 2018) With beets. segmentation per pixel dataset -864 for and testing on Bonn2017). Optimizer: rmsprop between crops and Bonn2017 dataset - Batch size: 10 weeds. 2584 for Stuttgart initial learning rate: 0. 01 The spatial dataset The training stops after 200 organization of plants Acquisition: epochs. in a row using 3D An agricultural field convolutions on a robot is sued to record sequence of images. the dataset on a sugar beet farm near Bonn in Germany for three months in the spring of (continued on next page).
The enhanced collected guava disease-based data was then augmented RGB image dataset. using the affine transformation method. (Yang, Yang, He, Proposed LFC -Net, a Real-time strawberry disease 2 400 of 14 classes of Accuracy:92. 48% Zhang & He, 2022) Location network, a Feedback classification and analysis strawberry disease images. Precision: 90. 68% network, and a Classification systems in the cloud service. Acquisition: Recall: 86. 32% network of strawberry A self-supervision Images are collected by web F1 score: 88. 45% diseases in real planting mechanism allows the model scraping environments to identify diseased regions With in strawberry images Optimizer: SGD, Epoch: 5 without annotations such as 000, Batch size: 32, and the bounding boxes. initial learning: 1e-4 (Kerkech et al. , 2020) SegNet (Badrinarayanan, Mildew disease detection Combination/fusion of the 4 classes (shadow, ground, Leaf-level eval average Kendall & Cipolla, 2017) for in the vine field using a visible and infrared UAV healthy result Segmentation Network-based deep learning images to improve disease and symptoms) for Accuracy: 82.
20% for LeNet5 segmentation approach detection. 70,560 patches images of size fusion Visible range (17, 640 samples for each intersect infrared range class 90. 23% for fusion Visible Acquisition: range union infrared. The UAV Quadcopter drone Grapevine-level average was used in the data result acquisition. Accuracy: 88. 14% for fusion Visible range intersect infrared range 95. 02% for fusion Visible range union infrared. (Y. Guo et al. , 2020) VGG16 and RPN algorithms Plant disease detection CNN and RPN algorithms are Training data includes Plant Accuracy: 83. 75% are used and recognition. used to recognize and locate Photo Bank of China (PPBC) With disease leaves in real or acquired by Crawler Optimizer: SGD complex environments. technology and plantVillage Batch size 256 sub-dataset: 537 black rot, Learning rate 0.
input image into different Cameras are used to capture the 83. 23% Optimizer: SGD resolutions to improve test images mini-batch size of 2. performance Momentum: 0. 9 learning rate: 0. 02 (Dawei et al. , 2019) CNN (AlexNet) Detection and recognition of Human experts check the 484 images of ten types of pests Accuracy: With pests using transfer learning classifier's performance. Data acquisition: 93. 84% Epoch: 10 A digital camera is used for some samples, while an online resource is used for others. (Thenmozhi & Proposed CNN (VGGNet) and The classification and Uses multiple datasets to Three datasets: Accuracy: Reddy, 2019) transfer learning. identification of crop (rice, validate their methodology. 40 classes of insects with NBAIR ( 96. 75%, With wheat, maize, soybean, and NBAIR, 2022), 24 classes of Xie1 ( 97.
47%, and Optimizer: SGD sugarcane) insect pests. Xie et al. , 2015), and 40 classes of 95. 97% Learning rate: Xie2 (Xie et al. , 2018). for insect 5e-5, 1e-4, 5e-4 and 1e-3 datasets of Batch size: 10-130. NBAIR, Xie1, and Xie2. (C. -J. Chen et al. , CNN (YOLOv3) and Long An agricultural system that Sensors are used to analyze 687 images of Tessaratoma Accuracy: 90%; 2020) Short-Term Memory (LSTM) uses environmental the life cycle of pests so that papillosa. mAP: 92% to pests predict. information from weather farmers can be notified Acquisition: With stations and IoT sensors to quickly of pest appearances Mobile apps and drones are used Optimizer: SGD detect pests. and damage. to collect Tessaratoma papillosa Learning rate: 1e-4 images.
2 megapixels). and 26 diseases, labeled and categorized unhealthy leaf images. 38 categories by species and into 38 plant-disease pairs, are available in disease. three versions: colored, grayscale and segmented IP102 (Wu et al. , 2019) A large-scale benchmark 75, 222 images for 102 classes Internet as the primary source to Pests are often difficult to differentiate Dataset for insect pests collect images (Google, Flickr, Bing, because the object colors and background etc. ) and insect science websites are similar. The images contain throughout the life cycle of the pests, and they are difficult to classify, especially during the larval period. Early crop dataset 1 ( Crop and weed > 200 images for four classes Pests are often similar to each other. This repository contains field images of AUAgroup, 2019/2021b) identification early-stage tomatoes, cotton, velvetleaf, Plant Seedlings Dataset 2 ( Weeds and Crops 5539 plant images (RGB, Dataset is collected by the Aarhus and black nightshade Each class contains colorful images that Giselsson, Dyrmann, classification segmented) for 12 species University Signal Processing group show plants in different stages of growth.