dataesr/scientific-paragraphs-categorization huggingface dataset:
They employ quantum bits (qubits) that can be entangled, giving them the ability to manipulate vast amounts of data with few operations, and thus can solve problems polynomially faster than classical computers (i. e. , pre-exascale and exascale supercomputers) ultimately to more rapidly design better crops. Researchers can simulate quantum circuits on classical computers using free and open-source software development kits such as Cirq or Qiskit, and the cuQuantum software library, to leverage the power of GPUs and parallel computing to perform faster calculations. Examples of classical and quantum computers are compared based on their peak performance that is the theoretical highest processing power they can reach. For classical computers, the LINPACK benchmark tests the performance in double precision (64-bit) compute capabilities, whereas HPL-AI scores performance based on mixed precision  (16-and 32-bit).
Abbreviations: CNN, convolutional neural network; in the DMZ for access control, and are forwarded to the transfer nodes (typically Linux servers) to reach the filesystem where data can be transformed before or after storage using ETL or ELT, respectively (Figure  2 ). (C,D) Representative free and open-source software frameworks and their supported programming languages used to implement AI algorithms. Abbreviations: CPU, central processing unit; ELT, extract, load, transform; ETL, extract, transform, load; FLOPS, floating-point operations per second; GPU, graphics processing unit; HPL-AI, high-performance LINPACK for accelerator introspection; LINPACK, linear equations software package; PB, petabyte. Trends in Plant Science a human-in-the-loop (HITL) for harmonious human and AI system symbiosis.
AI architecture design (A) AI algorithms Designing human-centric X-AI for plant phenomics is not without challenges; it requires a dedicated and multidisciplinary team effort involving plant scientists, data scientists, and IS scientists to bring AI to its most feasible, desirable, viable, and responsible state. This novel multidisciplinary knowledge is clearly imperative to identify and reduce AI biases, and to facilitate explainability and accountability. We advocate that such a system architecture should constantly realign data architecture and technology infrastructure to serve novel AI architecture designs. Phenotyping complex traits demands the integration of data on different morphological, physiological, temporal, geospatial, and environmental variables  [35, 58]. Although large datasets are vital for creating accurate AI models and validating their results, storing them in a FAIR manner can be challenging.
Trends in Plant Science Data architecture plays a fundamental role in meeting these requirements. It consists of a set of standards that govern which data are collected, whether they should be transformed (e. g. , data cleaning, deduplication, format conversion, structuring, validation, etc. ) before or after storage using extract, transform, load (ETL) or extract, load, transform (ELT) processes, and where (data warehouses or data lakes) and how (matrices, cubes, polytopes, or distributed in-memory) the data are stored (Figure  2 ). Without AI, these datastreams would be overwhelming and chaotic  [35] , but reaching the full potential of AI-based analysis of large phenomic datasets depends upon the right technology infrastructure which defines the components that serve as a foundation for the data lifecycle, including hardware infrastructure, network flow, software frameworks, and programming languages (Figure  3 ).
High-performance computing (HPC), including pre-exascale supercomputers, is boosting both the accuracy and predictive power of these approaches. Although central processing units (CPUs) maximize the performance of an algorithm, graphics processing units (GPUs) can dramatically increase AI training speed because their processing cores were initially designed to process visual data such as images and videos  [11]. For example, to take advantage of GPUs, the compute unified device architecture (CUDA) software framework provides a development environment for creating and optimizing AI applications on GPUaccelerated local computers or supercomputers. However, CUDA works exclusively on Nvidia GPUs; alternatively, the open computing language (OpenCL) and openACC frameworks work on multiple types of GPUs  [59].
Another option is to translate automatically CUDA source code into a portable heterogeneous computing interface for portability (HIP) using source-to-source translators such as HIPify, so that non-Nvidia GPUs can benefit from the rapid development of CUDA applications. In addition, software libraries such as kokkos, RAJA, open multi-processing (OpenMP), and one application programming interface (oneAPI) can be leveraged to unlock the promise of heterogeneous computing where compute nodes employ more than one type of processor, including CPUs, GPUs, and tensor processing units (TPUs), among others. This enables the development of scalable AI-based applications in a hardware-agnostic way. With the advent of exascale computing, supercomputers will deliver higher performance in pattern searching in phenomic big data, and will thus boost AI abilities in digital phenomics, speeding up crop design (Figure  3A ).
However, building powerful supercomputers is a never-ending race, and as new ones are launched the number of compute nodes they comprise increases. For example, the first supercomputer to break the exascale barrier, Summit, comprises 4608 compute nodes, whereas the most powerful exascale supercomputer that tops the latest TOP500 list i , Frontier, contains 9472. This makes it more difficult to exploit supercomputers efficiently because of their need to transmit data back and forth between their nodes while running huge numbers of computations at the same time  [60]. Implementing AI algorithms (Figure  4A ) for such parallel computing is not easy. Luckily, emerging free and open-source software frameworks such as Tensorflow Keras, PyTorch, scikit-learn, and XGBoost, among others, as well as software libraries such as cuNumeric, are enabling scalability in parallel computing.
More powerful exascale supercomputers are being anticipated  [61] , and researchers may start to utilize quantum computers at some point in the future  [62]. This will ultimately drive digital phenomics toward designing faster, better crops and providing sustainability-friendly solutions (Figure  3A ). In addition to hardware infrastructure, properly designed network flows (Figure  3B ), such as the 'science demilitarized zone (DMZ)' that includes network architecture and performance tools  [63] , enable high-throughput access to datasets in a secure and timely manner while conforming with the FAIR data principles  [39]. Software frameworks (Figure  3C ) provide a working environment that helps researchers to achieve higher productivity in designing AI algorithms; they support more than one programming language (Figure  3D ), enabling fast and efficient implementation of algorithms without compromising code quality.
Starting from the top of the bias cascade, at the data collection or selection step, the minority of data that do not conform to the general characteristics of a given dataset, known as outliers, should be removed during data cleaning to mitigate 'measurement' bias. Regarding 'label' bias, data annotators should be supplied with detailed instructions containing visual examples of the correct output for a given input to be able to reduce ambiguities and avoid mistakes that result from incorrect or incomplete knowledge. For example, when labeling weed species, in addition to their morphological descriptions, a visual representation of each species could be helpful for annotators. Next, 'sample selection' and 'group attribution' biases can be mitigated by establishing random sample selection and statistical correction processes  [64, 65].
When preprocessing data, intrinsically unbalanced datasets can be balanced by means of oversampling (i. e. , augmenting the number of training examples in the minority class to be equivalent to other classes) and/or undersampling (i. e. , reducing the number of training examples in the majority class to be equivalent to other classes)  [11]  to eliminate 'class imbalance' bias  [66]. Properly sampled and preprocessed data mitigate the risk of 'correlation fallacy' and 'apophenia' biases, which can occur during the model development step. When training, 'overfitting' can be debiased by increasing the size of the training data, decreasing the model complexity, or ignoring the less important features in a process called regularization  [67]. 'Underfitting' bias can be resolved by increasing the complexity of the model to capture nonlinear relationships in data.
When new regulations enter into force, post-authorization monitoring of AI applications becomes crucial to ensure that the performance of models does not degrade in practice. Once a model has passed regulatory authorization and is implemented in phenomics, it needs to be retrained periodically using new datasets to prevent it from becoming outdated, thus ensuring 'domain shift' bias mitigation. Reducing the risk of bias in AI models requires continuous human attention across the five development steps, keeping HITL. Studies have shown that human-computer interaction in HITL AI has improved the predictive performance of AI-based image analysis and reduced biases  [68] [69] [70]. HITL can make a significant impact on phenomic data collection, data preprocessing, model development, evaluation, and deployment.
The code and computational notebooks are open source and freely accessible through our GitHub repository vi. Collectively, this will accelerate the rate of discovery and move toward open science and AI ethics in digital phenomics. In our tutorial we train the 'this looks like that' algorithm to classify diseases using the crowdsourced cassava disease classification dataset. This choice is motivated by the importance of cassavaa key food security crop grown by smallholder farmers in Africa, Asia, and South America. However, diseases that plague this crop are a major cause of poor yield  [93]. Existing methods to identify diseases require governmental agricultural experts to visually inspect and diagnose the plants  [94].
This description covers relevant factors such as data preprocessing, image classes and formats, the architecture of the model, model training and evaluation, prototypical explanations, and the computer cluster used for training. Dealing with small datasets Because some phenotyping experiments generate small amounts of data, X-AI models have fewer training examples to learn from. How can models learn well from small datasets? Low-complexity models that have a small number of trainable parameters can perform better than complex models because they are less prone to overfitting and generalize better  [95]. In addition, TL can be employed to transfer knowledge acquired while learning a different but related task from a model trained on a large dataset to fit a new model using a small dataset  [96].
When TL is not sufficiently powerful because of the absence of large datasets, cumulative learning (CL) can be used to train a model over various small datasets and accumulate knowledge in the resulting network representation (i. e. , model weights)  [97]. Even when pretraining a model with TL or CL is not possible, the cosine loss function can substantially improve the predictive performance of the model  [98]. Whereas the loss function of the model measures the error between the input and predicted output, the cosine loss function maximizes the cosine similarity between them. One-shot or few-shot learning can also be used to train a model from one or a handful of training image data by basing predictions on a similarity metric (e. g. , cosine similarity) that compares training data to new inputs  [99].
Most recently, with the embedding of human knowledge into AI, it will be possible to supplement small training datasets. Representation of such knowledge can be incorporated into AI by means of changes to the input data and loss function  [100] , to the architecture of the algorithm  [101] , or to a combination thereof. Alternatively, oversampling can be another workaround that produces new sample data to augment small datasets. These new data, however, should be meaningful, sufficient, and realistic, and should contribute to better performance of predictive models  [102]. Oversampling can be achieved by (i) performing geometric transformations on existing images using primitive data-manipulation techniques, including flipping, rotation, shearing, cropping, and translation  [103] ; (ii) generating new synthetic data with generative adversarial Figure  5.
Cultivating the conditions for explainable artificial intelligence (X-AI) to flourish in plant digital phenomics. Data preprocessing prepares input data for X-AI algorithms: descriptive data analysis provides statistical summaries about a dataset to detect anomalies; data annotation and standardization are performed by labeling and adding relevant, structured information about the data such as their source and other details known as metadata; and feature engineering uses existing features to create new ones whereas feature selection extracts relevant features from the complete set of features in a dataset, thus increasing the predictive precision of learning algorithms. X-AI can be achieved by either opening the black box or designing a transparent glass box. X-AI can be interrogated to understand why a decision has been made, keeping human-in-the-loop (HITL) of such decision making, and allowing two-way transfer of knowledge where, on the one hand, experts assist in the training of X-AI and, on the other hand, explanations can be used to generate scientific hypotheses that can result in new discoveries.
For example, addressing the problem of bias in phenomics AI requires the integrated knowledge of socially and intellectually diverse researchers who specialize in plant science, plant phenomics, plant pathology, data science, computer science, IS, social science, and bioethics, to name but a few. Second, we emphasize the crucial importance of an open science system that aspires to open access not only for research outputs but also for the whole research process, and posit that all phenomics and data centers should participate in these practices. Promotion of open science and team science are synergistic goals, both of which are essential for improving our knowledge and scientific rigor. Figure  6. Planning, training, and interpreting an explainable artificial intelligence (X-AI)-based analysis in plant digital phenomics requires careful consideration at each stage of the analysis.
This figure sheds light on all the elements of designing such a workflow using a cassava leaf disease classification task as an example. For data preparation (i) a dataset shared on Kaggle by the AI laboratory at Makerere University was used for analysis; (ii) data cleaning was carried out to eliminate outliers and mislabeled images; (iii) the dataset was randomly split for training, validation, and testing; (iv) another shared version of the dataset with images cropped to leaf boundaries using a trained YOLO model was used to minimize noise in training images; and (v) the training dataset was augmented and balanced by oversampling and creating random transformations to image geometries. An alternative solution to oversampling is to synthesize leaf images; OpenCV can be used to segment leaves to train a deep convolutional generative adversarial network (DCGAN) to generate synthetic data.
A 'this looks like that' interpretable by design algorithm, implemented in Python and PyTorch, was carefully chosen for the classification task; its training time was approximated and compared on different hardware, showing the advantages of GPUs over CPUs and exascale over pre-exascale supercomputers. However, increasing the number of GPUs comes at the price of increased network communication and input-output (I/O) operations to synchronize the model over cluster nodes. Such overheads can cause a delay in the training time. For example, although the algorithm is expected to complete 1000 training epochs in 31 h using 26 Nvidia Tesla V100 GPUs, it is still expected to take ~8 h using the full power of the Summit supercomputer (27 649 GPUs). The 'this looks like that' algorithm uses transfer learning to import convolutional layers from pretrained models and, during training, the prototype layer extracts parts of training images (prototypes) and learns a similarity metric between them; the final class prediction is based on the weighted sum of similarities between the input and prototypes.
Concluding remarks and future perspectives We are experiencing an unprecedented time in which the availability of vast amounts of phenomic data, combined with advances in AI, are providing the opportunity to turbocharge the data to insight journey. This opportunity is an incentive not only to design and implement effective and reliable data management strategies but also to improve the visibility, accessibility, and usability of publicly available datasets that can support research and innovation in plant digital phenomics. Although AI has demonstrated impressive potential in phenomics, risks due to bias and lack of transparency of models should be considered. Reducing these risks entails multidisciplinary science and technology teams working together. The involvement of plant scientists, data scientists, and IS scientists during the complete lifecycle of AI analysis is integral to ensuring explainability and to identifying bias in the predictive models.
Quantum computers (e. g. , System One of International Business Machines and H1-2 of Quantinuum) represent a new paradigm in computation that leverages the fundamental principles of quantum mechanics to perform calculations. They employ quantum bits (qubits) that can be entangled, giving them the ability to manipulate vast amounts of data with few operations, and thus can solve problems polynomially faster than classical computers (i. e. , pre-exascale and exascale supercomputers) ultimately to more rapidly design better crops. Researchers can simulate quantum circuits on classical computers using free and open-source software development kits such as Cirq or Qiskit, and the cuQuantum software library, to leverage the power of GPUs and parallel computing to perform faster calculations. Examples of classical and quantum computers are compared based on their peak performance that is the theoretical highest processing power they can reach.
For classical computers, the LINPACK benchmark tests the performance in double precision (64-bit) compute capabilities, whereas HPL-AI scores performance based on mixed precision(16-and  32-bit). Because quantum computers use quantum processing units (QPUs) to manipulate the quantum states of qubits to perform computations, their performance is measured using quantum volume (QV). (B) Network flow to enable high-throughput access to and sharing of phenomic datasets. Requests coming from the wide area network are forwarded through a router to one of two paths: (i) the data query and browse path (red line) where requests to browse or search phenomic datasets are filtered through a firewall and processed by the hosting server, and (ii) the data transfer path (green line) where requests to download or upload phenomic datasets are inspected.
(B) Network flow to enable high-throughput access to and sharing of phenomic datasets. Requests coming from the wide area network are forwarded through a router to one of two paths: (i) the data query and browse path (red line) where requests to browse or search phenomic datasets are filtered through a firewall and processed by the hosting server, and (ii) the data transfer path (green line) where requests to download or upload phenomic datasets are inspected (Figure legend continued at the bottom of the next page. ).
Acknowledgments The authors are grateful to the editor and three anonymous reviewers for their constructive and insightful comments which greatly helped to improve the manuscript. Partial support for this work was provided by the  EU   FP7  project  WATBIO  (grant  311929 ); the  EU   H2020  project  EMPHASIS-PREP and its Italian node PHEN-ITALY  (grant  739514 ); and an  Italian Ministry of University and Research Brain Gain Professorship  to A. L. H,; as well as the  Center for Bioenergy Innovation , a  US Department of Energy (DOE) Bioenergy Research Center , the  Plant-Microbe Interface Science Focus Area (SFA) , and the integrated Pennycress Resilience Project, all supported by the  Biological and Environmental Research in the DOE Office of Science ; the  Oak Ridge Leadership Computing Facility , a  DOE Office of Science User Facility  supported under contract  DE-AC05-00OR22725 ; and the  DOE, Laboratory Directed Research and Development funding ORNL AI Initiative  project  ID  10875 at the  Oak Ridge National Laboratory.
g. , benchmark (e. g. , Graham: 2. 6 Summit: 1. 4 exaFLOPS; Frontier: 6. 86 petaFLOPS; Sierra: 125 exaFLOPS) petaFLOPS) Pattern searching in big Faster pattern searching in big Searching big phenomics data to uncover patterns in seconds; phenomics data phenomics data Accessing all items in a database at the same time in seconds Crop design Speeding up crop design Debugging millions of lines of software code Leveraging AI abilities for digital phenomics Boosting AI abilities for digital phenomics Providing sustainability-friendly solutions (B) Wide area Router Firewall Hosting ELT network server Tensorflow Keras Python, JavaScript, C++, Java PyTorch Python, C++, Java scikit-learn Python Demilitarized ETL Filesystem zone (data store) XGBoost Python, C, C++, Java, Julia, R, Ruby, Swift Query/browse path Data transfer path Data transfer node Apache MXNet Python, C++, Java, Scala, Julia, Clojure, R, and Perl.
aestivum Wheat Wheat Wheat Country of origin UK UK Japan, France, Canada, UK, Switzerland, China, Australia Japan, France, Canada, UK, Switzerland, China, Australia, USA, Mexico, Republic of Sudan, Norway, Belgium KS, USA Dataset Supporting data for 'deep machine learning provides state-of- the-art performance in image- based plant phenotyping' Wheat 2017 Global wheat head detection (GWHD) 2020 GWHD 2021 Supporting data for 'high throughput phenotyping with deep learning gives.
aestivum Brassica napus Arabidopsis thaliana Buckwheat Sunflower A. thaliana A. thaliana A. thaliana A. thaliana Country of origin UK USA UK Australia France Italy Dataset insight into the genetic architecture of flowering time in wheat' RootNav 2. 0 Cosegmentat- ion for plant phenotyping (CosegPP) Aberystwyth leaf evaluation dataset Deep phenotyping dataset Supporting data for 'ChronoRoot: high- throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture' Plant.
vinifera Grapevine Grapevine Mangifera indica Dataset Country of origin ACFR orchard Australia fruit dataset Dataset for Indonesia pest classification in Mango farms Berries in Germany vineyards: color (BIVcolor) Supporting Spain data for 'a novel ground truth multispectral image dataset with weight, anthocyanins and Brix index measures of grape berries tested for its utility in machine learning pipelines' ESCA dataset Italy An annotated France image dataset of downy mildew symptoms on Merlot grape variety The MangoNet India semantic.