dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010)  and the ERA5 Land  (Muñoz-Sabater et al. , 2021). The extraction of the ERA5 Land data is done via the dedicated API. SAFRAN consists in reanalysis of climate variables at 8 km spatial resolution and hourly timescale over France starting 1958. In this paper, the water module is deactivated and only the weather data (Rg and T a) is extracted from the Météo-France SAFRAN dataset and re-projected over the UTM/31N at 8 km resolution. PROSAIL was extensively used as a radiative transfer model for vegetated areas over a wide range of inversion schemes  (Wang et al. , 2022). PROSAIL combines the PROSPECT and the SAIL models  (Baret et al. , 1992). PROSPECT provides leaf spectral properties in the 400 nm to 2500 nm band width  (Jacquemoud and Baret, 1990). SAIL (Scattering by Arbitrary Inclined Leaves) is a multidirectional canopy reflectance model  (Verhoef, 1984)  based on the bidirectional reflectance model  (Suits, 1971).
Validation datasets The validation is based on several datasets covering the main output variables of AgriCarbon-EO: CO 2 flux measurements (i. e. Net Ecosystem Exchange, N EE; Gross Primary Production, GP P ; Ecosystem Respiration, Reco); Dry Aboveground biomass, DAM measurements, and yield maps. A summary of the ID and characteristics of the aforementioned validation datasets is presented in Table  4. The validation datasets are extracted from the database of the Environmental Information System the laboratory and the Regional Spatial Observatory (RSO). his Information systhem centralises the in-situ datasets of the RSO including the data from the ICOS flux towers located in the RSO (e. g. FR-AUR) (SIE, 2022). In addition to the validation exercise, the large scale spatial maps from AgriCarbon-EO are analysed with respect to soil texture provided at 250m resolution from SOILGRIDS and Digital Elevation Model (DEM) provided by the European Environmental Protection Agency (EPA).
Limitations of the Bayesian and physically based approach While the components of AgriCarbon-EO have been tailored to the requirements mentioned in the introduction (large scale, high resolution, uncertainty estimates, and biophysical processes), we showed limits for each of them. For instance we show that the BASALT Bayesian approach can be sensitive to an erroneous observation associated with a low uncertainty (Figure  11  d). A trade-off has to be made between the variability of the generated solutions, and the number of LUT entries to maintain computational efficiency. A solution could be to consider a joint distribution for prior parameters to propose a better ratio of appropriate solutions(Figure10),  (Wang et al. , 2022). On one hand, the radiative modelling is constrained by the spectral library database  (Verhoef et al.
, 2007) , which may not reflect ground conditions like presence of weeds impacting GLAI retrievals. On the other hand, the crop model predictions will depend on fixed and prior parameters of a given crop, and the a posteriori parameters distribution. Note that ixed prior parameters need to be chosen through agronomic knowledge and bibliographic evidence. Alternatively, we could have reverted to machine learning approaches that have gained in popularity recently for precision agriculture and soil carbon farming application  (Sharma et al. , 2021). But, while they are powerful tools to extract the most of existing training data, they are also constrained by the extent of the observation dataset. Also, Machine Learning (ML) approaches predict a limited number of variables at once and may need regular retraining to take into account changing climatic conditions and management practices.
Such exercise, requires in addition to the new soil module parameters, input dataset on initial soil carbon content and organic organic amendments which is challenging for large scale applications. One way of achieving this is to take advantage of the rapidly developing Farm's Management Information Systems (FMIS) and enhanced soil properties maps through Digital Soil Mapping (DSM). Even though FMIS data are not easily accessible, it is expected that this limitation will be reduced with the development of soil carbon farming policies (like the Label Bas Carbon) and the auditing schemes  (de Gruijter et al. , 2016). Such data exchange will have a dual positive effect providing that adequate soil sampling protocols are applied. The SOC data will augment the data collection available for validation and verification of tools like AgriCarbon-EO, and at the same time, approaches such as AgriCarbon-EO may provide optimal sampling strategies for the estimation of SOC stock changes for carbon auditing.
Assessing the carbon budget components and the SOC stock changes at this resolution provides 1) a coherent spatial information with soil samples. 2) the means to provide better sampling strategies forsoil and plants in MRV approaches. In the future, we aim to provide further constraints to the assimilation scheme through the inclusion of additional remote sensing datasets from SAR and other variables derived from optical sensing. Such information will reduce the impact of cloud coverage and the equifinality in parameter retrievals. It is notable that AgriCarbon-EO can also provide variables related to the water cycle such as evaporation, transpiration, drainage as well as soil water content. In summary further studies will thus aim to use AgriCarbone-EO as a coherent and multi-criteria full crop cycle agronomic diagnosis tool for production, carbon, phenology and water use.
Acknowledgements. Data acquisition at FR-Lam and FR-Aur were mainly funded by the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS-INSU) through the ICOS and OSR SW observatories. We thank Jean-François Dejoux, Tiphaine Tallec, Franck Granouillac, Nicole Claverie, for their technical support. We extend special thanks to Mr. Andréoni (farmer) for accommodating measurement devices in their fields at FR-AUR. T. Wijmer thesis was financed by the NIVA project from ASP and "Naturellement Popcorn". Financial support was also obtained from the ERANET ANR SMARTIES project, the Horizon Europe ClieNfarm (n°101036822) and ORCaSa (n°101059863) projects and the Bag'ages (Agence de l'eau Adour Garonne) projects. This work was granted access to the HPC resources of CALMIP super-computing centre under the allocation 2022-P20013.
Datasets: 1. Remote sensing data for Sentinel-2 and Landsat8 using the maja processing are downloaded from THEIA : https://www. theialand. fr/en/product/sentinel-2-surface-reflectance/. The Sentinel-2 level 2A and Landsat8 L2A data are distributed under the ETALAB V2. 0 open license. 2. Land Cover datasets are available at : https://geoservices. ign. fr/rpg 3. Validation datasets are available from the SIE website : https://sie. cesbio. omp. eu/ 4. Soil texture are available at: https://www. isric. org/explore/soilgrids : https://doi. org/10. 17027/isric-wdcsoils. 20190901 5. DEM dataset are available at: https://www. eea. europa. eu/data-and-maps/data/eu-dem Outputs: 6. Full dataset of all simulations is about 5T of memory, selected outputs can be made available upon request to the authors.
7. Output maps for Wheat 2017 are available at : 10. 5281/zenodo. 7534280 Code availability: AgriCarbon-EO is implemented in python3. Agricarbon-EO requires the PROSAILv5 python package and the SAFYE-CO2 v2. 0. 5 python implementation. AgriCarbon-EO v1. 0. 1 is available free of charge for research and evaluation purposes (non-commercial) upon signature of a license agreement with Toulouse Technology Transfer (TTT) office of Université Toulouse 3. For this, the user contacts the TTT at "contact@toulouse-tech-transfer. com" providing his contact information, affiliation, and objective of use. Upon validation of the license, the code is provided by the team at CESBIO. SAFYE-CO2 v2. 0. 5 is provided with AgriCarbon-EO v1. 0. 1 in this same procedure. Note that for this paper, and in compliance with the journal requirements, an anonymous procedure was put in place to grant access for the reviewers.
Wang et al. , 2022). Deep Learning (DL), a subfield of machine learning (ML) distinguished by the use of Artificial Neural Networks (ANN), is currently positioned at the forefront of advancements in weed identification  (Nasiri et al. , 2022). Its capacity to extract nuanced features from data and identify complicated patterns has significantly enhanced the precision and effectiveness of weed detection  (Ghatrehsamani et al. , 2023). Deep learning models, which have been trained on extensive datasets, demonstrate an unparalleled capacity to accurately differentiate between crops and weeds, even in complex and dynamic agricultural environments  (Rai et al. , 2023). Furthermore, DL has made substantial contributions to various aspects of precision agriculture, encompassing identification and counting of crop plants  (Rai and Flores, 2021) , detection of diseases in crops  (Liu and Wang, 2021) , identification of crop stress  (Butte et al.
, 2023; Page et al. , 2021b) , as a methodological framework to investigate and consolidate prior research undertaken on the application of artificial intelligence in weed management, with a specific focus on weed identification and deep learning in agriculture. This framework includes four phases: (1) identification, (2) screening, (3) eligibility, and (4) inclusion. The search was conducted utilizing the Scopus and Web of Science (WoS) databases, combining the terms "weed management", and "artificial intelligence". This yielded a total of 5821 outcomes, with 5713 from Scopus and 108 from WoS. To ascertain the final survey terms, the aforementioned data were exported and thereafter inputted into the VOSViewer software  (Waltman and Ecken, 2010). The results of the software are depicted in Fig.
, 2021). The selection of appropriate AI algorithms, the design of training datasets, and the calibration of weed detection models are influenced by the country factor. Moreover, it facilitates the incorporation of AI models into specific agricultural settings, ensuring that weed management approaches are in accordance with local limitations and preferences. Considering the importance of the country of origin within the context of AI-enabled weed management, it is essential to take into consideration the countries that are documented in the publications indicated (Fig. 8 ). Within the scope of this analysis, China (17) emerges as the nation to which most articles are referring to perform experiments, followed by the USA (12). In addition, the precise identification of weed species plays a crucial role in weed management as weed species can vary widely in their growth habits, resistance to herbicides, morphological characteristics, and competitive abilities.
AI implementation The integration of AI into weed management practices signifies a transformative stride in contemporary agriculture. This technology not only offers the potential for improved efficiency and effectiveness in weed control but also aligns with the emerging priorities of sustainability and precision agriculture. This thematic group of criteria pertains to the system that is employed in weed management. These criteria encompass (i) the functionality of AI, (ii) the capture technology, the vital bridge connecting the physical agricultural environment to the digital realm of AI, (iii) the training dataset, the foundation upon which AI models learn and adapt, (iv) the AI model, the intricate algorithmic brains that drive decision-making, and (v) outcomes and accuracy, the ultimate litmus test of AI's practical value in weed management.
Training dataset In conjunction with capture technology, the availability of highquality training datasets is essential for AI model development. Training datasets, comprising labeled images and data, enable AI models to learn and make accurate weed identification and management decisions  (Bochtis et al. , 2022b). Training datasets serve as the foundational building blocks upon which AI models learn and develop the capability to make informed decisions regarding weed identification and management  (Sudars et al. , 2020). The quality, diversity, and representativeness of these datasets profoundly influence the accuracy and reliability of AI-driven weed management systems  (Olsen et al. , 2019). The size and volume of training datasets can significantly impact the performance of AI models. Larger datasets, generally, lead to more robust and accurate models.
Nevertheless, the process of gathering and labeling vast datasets can be a demanding task. The utilization of labeled data, wherein each image or data point is annotated with explicit information indicating the presence or absence of a weed, is an essential requirement in the context of supervised machine learning  (Shorewala et al. , 2021). It enables AI models to establish correlations between distinct patterns and characteristics and needs to be as precise as possible, as it is of utmost importance in the training process.
, 2022). ROC curves plot the true positive rate against the false positive rate, providing insights into a model's discriminatory ability across different decision thresholds, while AUC-ROC quantifies the overall discriminative power of the model, with higher values signifying better performance. Last but not least are mean Average Precision (mAP) value and Intersection over Union (IoU) metrics. mAP is a comprehensive metric used to evaluate the model's ability to accurately and effectively identify and categorize objects present in a picture or dataset, while IoU is a metric used to evaluate the accuracy of object localization in tasks like image segmentation and object detection  (Saqib et al. , 2023; Xu et al. , 2023). Subsequently, Mean Intersection over Union (MioU) is also referred in the literature, which is the mean value across all classes or category of objects in an image or a dataset  (Wang et al.
Discussion The preceding sections have meticulously examined the multifaceted landscape of AI in weed management, focusing on weed identification in agriculture, in the context of six thematic foci. In light of the Fig. 13. Quantity of articles referring to the related impact. complexities inherent in this technological shift, it is crucial to partake in a discourse that synthesizes the key findings and elucidates the ramifications of weed control facilitated by artificial intelligence. In this discussion, impacts of AI on food safety, its role in enhancing weed management effectiveness, and its contribution to eco-friendliness through herbicides reduction are contemplated. Furthermore, the broader context of AI implementation, the significance of training datasets and AI models, and the importance of accuracy and performance metrics in the successful adoption of AI systems are considered.
The successful implementation of AI in weed management is influenced by several critical factors. The choice of capture technology, encompassing sensors, drones, and other data acquisition tools, profoundly influences data quality and model accuracy; the construction of AI models relies on the foundational role of meticulously collected and comprehensive training datasets; and the selection of AI models, whether conventional machine learning or deep learning, dictates the level of precision in weed identification. Furthermore, the importance of accuracy and performance metrics cannot be understated; these metrics guide model refinement and validation, ensuring reliable results in realworld applications. The synergy between these factors underpins the comprehensive endeavor of AI implementation in weed management, offering a potent pathway towards agricultural innovation and weed and crop identification.
Acknowledgement This project has received funding from the European Union's HE research and innovation programme under grant agreement No 101084647. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for any use that may be made of the information the document contains. The publication of the article in OA mode was financially supported in part by HEAL-Link.
There are articles trying to increase the effectiveness of weed management by utilizing AI techniques. Herbicide reduction Efforts are undertaken to -eco-friendliness minimize or eradicate the utilization of herbicides. 4. AI implementation Functionality Purpose of using artificial intelligence in weed management. Capture technology The selection of the imaging equipment holds significance due to the distinct characteristics exhibited by each device. Some articles utilize open-source datasets to train and test their algorithms. Training dataset The number of images used for training the AI model is recorded.
54% None No 2022) identification camera -2017 × 2155 pixels Hussain et al. Weed detection 12000 Canon PowerShot SX540 tiny-YOLOv3 42% reduced sprayd liquid Robotics Yes (2020) and removal HS camera, Logitech C920 Webcam HD Pro (U. Farooq Weed detection 1300 Open-source dataset YOLOv4-tiny mAP = 85. 55%, average loss = IoT No et al. , 2022) 0. 25 Fatima et al. Weed detection 9000 1280 × 1024 pixel YOLOv5 mAP = 88% Robotics No (2023) and removal camera Modi et al. Weed 5094 48 MP Sony IMX586 DarkNet53 Accuracy 99. 1%, precision 99. 3% None Yes (2023) identification Sapkota et al. Weed 1230 100-MP FUJIFILM Mask R-CNN mAP = 80% UAV No (2022) identification GFX100 Xu et al. (2023) Weeds and crops 1181 Camera resolution 4864 * ResNet101_v and DSASPP accuracy of 0. 905 UAV No detection 3648 pixels IoU score of 0.
959 Kong et al. Weeds and crops 2140 Oneplus8P -48 MP YOLOv5 mAP = 84. 3% None No (2023) identification camera Barnhart et al. Weeds and crops 4492 Zenmuse X5R RAW YOLOv5 mAP = 77%, highest F1 score = None Yes (2022) identification camera 72% Valente et al. Weed detection N/A Phantom 3 Professional MobileNet, VGG, Resnet, F1-Score = 78. 36% and AUROC = UAV No (2022) UAV's camera DenseNet, ShuffleNet, 93. 74% EfficientNet, MNASNet Danilevicz et al. Weed detection 9171 Open-source dataset, and Resnet18 average precision = 0. 86, UAV Yes (2023) DJI Phantom 4 intersection over union = 0. 60, and unoccupied aerial vehicle F1 score = 0. 70 (UAV) RGB camera Gallo et al. Weed detection 4405 DJI phantom4 pro YOLOv7 precision = 61. 3%, mAP 56. 66, UAV Yes (2023) camera, and open-source recall = 62. 1% dataset Costello et al.
Weed detection 21784 Nikon D2 with a 24-70 Yolov4 detection accuracy = 94% None No (2022) mm lens and a focal length of 70 mm Sapkota et al. Weed detection N/A DJI deep learning-based precision = 95. 44 ± 4. 27%, recall UAV No (2020) Phantom 4 Pro with RGB estimation = 95. 48 ± 5. 05%, F-score = 95. 56 sensor (12 megapixels) ± 4. 11% Moraitis et al. Weed detection 400 OV2640 camera Faster-RCNN-Inception-V2 maximum accuracy 92% Robotics No (2022) (G C et al. , Weeds and crops 3792 Google Pixel 5 mobile SVM and VGG16 average f1-score >93% None Yes 2022) identification camera Hu et al. (2020) Weed 17509 Open-source dataset Graph Weeds Net (GWN) top-1 accuracy 98. 1% None No identification Jin et al. Weeds and crops 11339 HV1300FC, DaHeng YOLO-v3, CenterNet, and F1 score = 97. 1%, precision = None No (2022b) identification Image Faster R-CNN 97.
1%, recall = 97. 0% Lou et al. (2022) Weeds and crops 1500 LR1601-IRIS LIDAR and 3D-CNN accuracy = 83. 32% Robotics No identification Pika L hyperspectral system Wang et al. Weeds and crops 11780 open-source dataset, encoder-decoder network, MIoU = 88. 91%, mean accuracy = None No (2020) detection Canon transfer learning 96. 12% 60D Dang et al. Weeds and crops 5648 Mobile phones >10 YOLOv3-YOLOv7 max mAP = 95,22 UAV No (2023) detection megapixels Hennessy et al. Weed detection N/A Canon T6 YOLOv3-Tiny F1-scores of up to 0. 97 None Yes (2022) DSLR camera, LG G6 smartphone, Logitech c920 (P. Wang et al. , Weed 14035 Nikon D5300 SLR YOLOv3, YOLOv5, and average accuracy: 91. 8%, 92. 4%, None No 2022) identification Faster R-CNN and 92. 15% respectively Subeesh et al. Weed detection 1106 Xiaomi Mi 11 × mobile Alexnet, GoogLeNet, InceptionV3: accuracy = 97.
7%, None Yes (2022) device's rear camera InceptionV3, Xception precision = 98. 5%, recall = 97. 8% Thanh Le et al. Weed detection 3380 VITA 2000 Faster RCNN, Inception- mAP = 0,555 None No (2021) ResNet-V2 (A. Wang et al. , Weeds and crops 5536 Open-source dataset TIA-YOLOv5 F1-scoreweed = 70. 0%, Apweed = None No 2022) detection 80. 8%, mAP = 90. 0% (continued on next page).
7% and ≥96% Robotics Yes ResNet50 Chen et al. Weed 5187 N/A DTL, ResNeXt101 F1 scores >95% None No (2022) identification Amarasingam Weed detection N/A Micasense Altum eXtreme Gradient Boosting testing accuracy >96%, validation UAV No et al. (2023) multispectral camera (XGB), Support Vector accuracy> 97% Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN) Zou et al. (2022) Weeds and crops 1200 RERVISION USB8MP02G U-net IoU≤88. 98% None Yes detection Rahman et al. Weeds and crops 848 4442 × 4335 pixels YOLOv5, RetinaNet, Highest accuracy mAP = 79. 98% None No (2023) detection camera EfficientDet, Fast RCNN and Faster RCNN Fathipoor et al. Weed detection N/A Open-source dataset U-net IoU = 65% None No (2023) (J. -L. Zhang Weeds and crops 1918 N/A SE-YOLOv5x precision = 97. 6%, recall = 95.
64% None No (2022) identification CenterNet, RetinaNet, Faster RCNN, and RFCN, Jin et al. Weed 24000 DSC-HX1, SONY® GoogLeNet, MobileNet-v3, accuracy≥0. 999, F1 scores≥0. 998 Robotics Yes (2022a) identification ShufeNet-v2, and VGGNet Gao et al. Weeds and crops 2723 Nikon D7200 tiny-YOLOv3 mAP = 0. 829 None No (2020) detection Xu et al. (2021) Weeds and crops 4750 Open-source dataset Xception, ImageNet, accuracy = 99. 63% None No detection XGBoost Ma et al. (2019) Weeds and crops N/A Canon IXUS 1000 HS SegNet, FCN, U-Net accuracy = 92. 7%, 89. 5% and None No identification 70. 8% respectively Huang et al. Weed detection 604 SZ DJI drone camera OBIA method accuracy = 66. 6% UAV Yes (2020) and mapping Li et al. (2021) Weeds and crops 180 N/A MobileNetV2-SSD accuracy = 94% Robotics No detection, and planting Kamath et al.
Weed 4950 Canon PowerShot YOLO-v2 accuracy = 90% None Yes (2022b) identification SD3500 IS, Sony Cybershot (DSC-W220) Peteinatos et al. Weeds and crops 93000 Sony Alpha 7R Mark4 VGG16, ResNet-50, and plant accuracy = 77%, weed None Yes (2020) identification Xception accuracy = 98% Xia et al. (2022) Weed N/A DJI Phantom camera N/A barnyardgrass accuracy = 81. 1% UAV Yes identification velvetleaf accuracy = 92. 4% Ajayi et al. Weeds and crops 254 DJI Phantom 4 drone YOLOv5 accuracy = 65, precision = 43, UAV No (2023) identification camera recal = 43% de Camargo Weeds and crops N/A Sony NEX 5N ResNet-18 accuracy = 94% UAV Yes et al. (2021) detection Ofori and Omar Weeds and crops 5539 Open-source dataset EfficientNet, transfer accuracy = 95. 44% None No (2021) detection learning Pérez-Porras Weed detection 6319 Sony FDR-AX100E YOLOv5 accuracy = 77%, mAP = 76.
6. 3); all code is available at https://github. com/zacharyrmiller/coexistence_random_tradeoff. Approximating the persistent set when n is large For a pool of n species with colonization rates sampled from some distribution with density f (x), we aim to calculate the probability that any particular set of species constitutes the unique equilibrium metacommunity, which we denote by S. It is then possible to derive the distribution of key quantities, such as the richness of the final coexisting metacommunity (S = |S |). To calculate these probabilities, we analyze the iterative process described above mathematically. Tilman 17 derived a formula for the niche shadow associated with species i; that is, a range c i < c j < ℓ i within which any inferior competitor j is excluded. Thus, rather than computing p ⋆ j to determine if species j is in the coexisting metacommunity (using the algorithm outlined above), one can equivalently compute ℓ i for each coexisting species and then check whether c j falls within this niche shadow.