Record of routine block pruning cycles by block segment.

This dataset contains vineyard images collected from 2021 to 2023. Labels were created for each image to indicate vine trunk, unpruned shoots, and pruned shoots. Thus, offering a comprehensive overview of the components of the vine. • The data collected include photos of vineyards at different periods to provide an overview that includes the main vegetative states of the vine. • The data can be used by researchers for computer vision projects, to build automatic pruning systems or other projects related to maintaining life. To date, no similar datasets are available, so this provides an important starting point in this area.

This dataset contains two data files that together contain the data needed to reproduce the analyses in "Self-pruning in tree crowns is influenced by functional strategies and neighborhood interactions" by Kothari et al., accepted in Functional Ecology. The first file is self_pruning_processed.csv, which contains data on the self-pruning behavior of individual measured trees (rows) in the IDENT-Montréal experiment, as well as additional data on neighborhood characteristics used in explaining the self-pruning behavior. The second file is plot_vars.csv, which contains information on experimental plots (rows) used to reproduce plot-level analyses. In addition, there are two metadata files that explain the interpretation and units of each of the columns in the data files.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

nthakur-backup/bge-retrieval-data-ivf-query-pruning-fixed-50K dataset hosted on Hugging Face and contributed by the HF Datasets community

The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive web-service implementing this algorithm. Compared to our previous method, the new algorithm is up to four orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world datasets, we show that, our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116,334 taxa each. Using simulated datasets we show that, when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum likelihood trees that are topologically closer to the respective true trees.

nthakur-backup/bge-retrieval-data-ivf-query-pruning-fixed-100K dataset hosted on Hugging Face and contributed by the HF Datasets community

Neural Network Pruning Tool Market Outlook

According to our latest research, the global Neural Network Pruning Tool market size in 2024 stands at USD 468 million, reflecting a robust trajectory driven by the increasing demand for efficient deep learning models. The market is exhibiting a strong compound annual growth rate (CAGR) of 22.1% from 2025 to 2033. By 2033, the market is expected to reach an impressive USD 3.08 billion, underscoring the pivotal role of neural network pruning tools in optimizing AI models for deployment across diverse industries. This growth is primarily fueled by the surging need for model compression and the proliferation of edge AI applications, as organizations seek to balance high performance with resource efficiency.

One of the primary growth factors propelling the Neural Network Pruning Tool market is the exponential rise in AI adoption across industries such as healthcare, automotive, BFSI, and IT & telecommunications. As organizations increasingly integrate AI-driven solutions into their workflows, the demand for more efficient, compact, and scalable neural networks has become paramount. Neural network pruning tools play a critical role in reducing model size and computational complexity without significantly compromising accuracy, thereby enabling faster inference and lower energy consumption. This is especially important for real-time applications in sectors like autonomous vehicles and medical diagnostics, where latency and resource constraints are critical considerations. Furthermore, as deep learning models become larger and more complex, the need for effective pruning solutions that can streamline deployment and maintenance continues to grow, further accelerating market expansion.

Another significant driver of market growth is the rapid evolution of edge computing and the increasing deployment of AI models on edge devices. Edge AI requires models that are lightweight, efficient, and capable of running on devices with limited computational power and memory. Neural network pruning tools are instrumental in achieving these objectives, as they allow for the deployment of high-performing AI models on smartphones, IoT devices, and embedded systems. The surge in smart devices, coupled with the proliferation of 5G networks and advancements in semiconductor technologies, has created a conducive environment for the adoption of pruning tools. As a result, enterprises are increasingly investing in pruning solutions to enhance the performance, scalability, and cost-effectiveness of their AI-driven products and services, thereby fueling market growth.

Moreover, the growing emphasis on sustainability and energy efficiency is shaping the trajectory of the Neural Network Pruning Tool market. Organizations are under mounting pressure to reduce the carbon footprint of their AI operations, especially in data centers and large-scale cloud environments. Pruning tools help achieve significant reductions in computational requirements and energy consumption, aligning with global sustainability goals. This trend is further reinforced by regulatory initiatives and industry standards that promote green AI practices. Additionally, the rise of open-source frameworks and the increasing availability of user-friendly pruning tools are lowering the barriers to entry for small and medium enterprises, democratizing access to advanced AI optimization techniques and driving widespread market adoption.

From a regional perspective, North America currently dominates the Neural Network Pruning Tool market, accounting for the largest share due to the presence of leading AI technology providers, robust R&D investments, and early adoption of deep learning solutions. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by the rapid digital transformation of emerging economies, government initiatives to promote AI, and the expansion of manufacturing and IoT ecosystems. Europe is also experiencing significant growth, particularly in sectors such as automotive and healthcare, where the demand for efficient AI models is high. The Middle East & Africa and Latin America are gradually catching up, supported by increasing investments in AI infrastructure and a growing pool of skilled professionals.

Correlated evolution among traits, which can happen due to genetic constraints, ontogeny, and selection, can have an important impact on the trajectory of phenotypic evolution. For example, shifts in the pattern of evolutionary integration may allow the exploration of novel regions of the morphospace by lineages. Here we use phylogenetic trees to study the pace of evolution of several traits and their pattern of evolutionary correlation across clades and over time. We use regimes mapped to the branches of the phylogeny to test for shifts in evolutionary integration while incorporating the uncertainty related to trait evolution and ancestral regimes with joint estimation of all parameters of the model using Bayesian Markov chain Monte Carlo. We implemented the use of summary statistics to test for regime shifts based on a series of attributes of the model that can be directly relevant to biological hypotheses. In addition, we extend Felsenstein's pruning algorithm to the case of multivariate Brownian motion models with multiple rate regimes. We performed extensive simulations to explore the performance of the method under a series of scenarios. Finally, we provide two test cases; the evolution of a novel buccal morphology in fishes of the family Centrarchidae and a shift in the trajectory of evolution of traits during the radiation of anole lizards to and from the Caribbean islands.

The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived from knowledge of surface water, the NHD model, and associated feature specification standards. Relative prominence of network features is estimated from upstream drainage area (UDA). Network and polygon features are pruned by UDA and NHD reach code to achieve a drainage density appropriate for any less detailed map scale. Data partitioning maintains local drainage density variations that characterize the terrain. For demonstration, a 48 subbasin area of 1:24 000-scale NHD was pruned to 1:100 000-scale (100 K) and compared to a benchmark, the 100 K NHD. The coefficient of line correspondence (CLC) is used to evaluate how well pruned network features match the benchmark network. CLC values of 0.82 and 0.77 result from pruning with and without partitioning, respectively. The number of polygons that remain after pruning is about seven times that of the benchmark, but the area covered by the polygons that remain after pruning is only about 10% greater than the area covered by benchmark polygons. ?? 2009.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The branching pattern of a tree determines the efficiency of light interception and carbon assimilation. Pruning can modify the branching pattern, because of changes in physiological and environmental conditions,and ultimately pruning can have major effects on yield. For one of the major tropical tree crops, cocoa (Theobroma cacao), very little is known about branching response to pruning.To address this knowledge gap, we performed a pruning experiment on young cocoa trees in Côte d’Ivoire.We applied five treatments: two heading treatments (the removal of the terminal apex or 66% of a primary branch) and two thinning treatments (the removal of 1 or 2 primary branches) and one unpruned control.The secondary-branching pattern of the primary branches was described by the number, position, and length of secondary branches right after pruning, and the same observations were repeated after a cycleof leaf production. The probability of branching and the length of secondary branches along a primary branch, in pruned and unpruned conditions, was analyzed using generalized mixed effect models.In unpruned conditions, the probability of secondary-branch presence was higher towards the middle of the primary branches and lower at the extremes.Secondary-branch length decreased going from the base to the tip of a primary branch. After one cycle of leaf production, new secondary branches emerged preferentially on the distal section of a primary branch,but probability of branch emergence was reduced by the presence of other secondary branches. Pruning increased the probability of branch emergence mostly towards the tip of a primary branch,with heavy heading having the strongest effect. By contrast, heavy thinning increased branch emergence also toward the base of the primary branch.Our results can be applied to improve formation pruning, as this may trigger branching in different parts of the crown, depending on the form of pruning. Our study also assists the development ofthree-dimensional tree models that could further our understanding of the impact of pruning on cocoa growth and productivity.

586 Global import shipment records of Pruning with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Robotic Tree Pruning Market Outlook

According to our latest research, the global robotic tree pruning market size reached USD 1.17 billion in 2024, driven by the rapid adoption of automation in agriculture, urban landscaping, and forestry management. The market is experiencing a robust growth trajectory, recording a CAGR of 14.6% from 2025 to 2033. With sustained investments and technological advancements, the market is forecasted to reach USD 3.62 billion by 2033. The increasing demand for precision agriculture and the growing emphasis on labor efficiency are among the primary growth factors propelling this industry forward.

The growth of the robotic tree pruning market is being significantly influenced by the accelerating trend of automation across agricultural and urban sectors. Labor shortages in traditional pruning and maintenance roles have compelled orchardists, vineyard managers, and municipal authorities to invest in robotic solutions that can deliver consistent, high-quality results with reduced human intervention. The integration of advanced technologies such as artificial intelligence (AI), machine vision, and sensor-based navigation has enabled robotic pruners to operate with remarkable precision, minimizing plant damage and optimizing growth cycles. Furthermore, the increasing focus on sustainability and resource optimization is leading to greater adoption of robotic systems that can reduce waste and improve overall tree health.

Another major growth factor for the robotic tree pruning market is the shift towards large-scale commercial farming and urban landscaping projects. As global food demand rises and urban green spaces become a priority for city planners, the need for efficient and scalable pruning solutions has intensified. Robotic pruners offer a compelling value proposition by enabling continuous operation, reducing operational costs, and mitigating the risks associated with manual labor, such as injuries and inconsistent quality. Additionally, the ability to collect and analyze data during pruning operations is empowering end-users to make data-driven decisions, further enhancing productivity and crop yields.

Technological innovation remains at the heart of market expansion. The integration of AI and machine learning algorithms allows robotic pruners to adapt to different tree species, recognize optimal pruning points, and even predict disease outbreaks. These capabilities are particularly valuable in high-value crops such as fruit orchards and vineyards, where precision is critical to maximizing yield and quality. Collaboration between robotics manufacturers, agricultural research institutions, and end-users is fostering the development of next-generation solutions that are more affordable, user-friendly, and adaptable to diverse environments. As a result, the market is witnessing strong interest from both established agricultural enterprises and emerging smart farming startups.

Regionally, North America and Europe are leading the adoption of robotic tree pruning technologies, supported by strong research ecosystems, favorable regulatory frameworks, and high labor costs. Asia Pacific is rapidly emerging as a high-growth region, driven by the modernization of agriculture in countries like China, Japan, and Australia. Latin America and the Middle East & Africa are also showing increasing interest, particularly in commercial orchards and urban landscaping projects. The regional outlook underscores the global relevance of robotic pruning solutions, with each market segment exhibiting unique drivers and challenges that shape adoption patterns.

Product Type Analysis

The product type segment of the robotic tree pruning market encompasses autonomous robotic pruners, semi-autonomous robotic pruners, and remote-controlled robotic pruners. Autonomous robotic pruners represent the most advanced and rapidly growing category, leveraging sophisticated AI and sensor technologies to perform complex pruning tasks with minimal human

Comparison of a conventional tending and pruning system (using handsaw and chainsaw) with a state state-of of-the the-art system (using electric pruning shears and the backpack brushcutter pole-clearing saw Husqvarna 535FBX ‘Spacer’); comprehensive field trial consisting of a time time and and motion study and ergonomic assessment through heart heart-rate measurements and posture analysis.

Abstract Physalis peruviana L. is a small fruit known for the great pharmacological importance, since it has high levels of vitamin A, C, iron, and phosphorus. Cultural practices such as training and pruning can influence the architecture of the plant, in such a way to obtain fruits of better quality. Therefore, the objective of this work was to evaluate the quality of fruits of Physalis peruviana L. produced in different systems of training and pruning. The work was carried out in Londrina-PR-Brazil using a completely randomized design, in a 4 × 2 factorial scheme containing four types of training (“UEL training”, “adapted inverted V training”, without training and “vertical training”) and two types of pruning (with and without prune) with four replicates. The analyzed variables were: fruit size and height, fresh weight with and without calyx, color (L *, C * and h°), number of fruits per plant, total soluble solids, pH, and acidity. The data were submitted to analysis of normality and homogeneity and compared by the Tukey test, at a 5% of probability level by the Sisvar program, and correlated by the Pearson test using the statistical program R. We conclude that plants conducted freely, had the highest values of fresh fruit mass with calyx. The conduction systems provide greater penetration of the solar radiation in the canopy, which favors, the accumulation of sugars and the intensity of the colorations of the calyxs. Plants conducted by the training systems UEL - State University of Londrina and adapted inverted “V” (both without pruning), obtained a greater amount of fruits. The pruning resulted in reduced fruit volume, lower values of soluble solids (SS), and dark calyx with a tendency to yellowish color.

Testingpruning

## Overview

Testingpruning is a dataset for object detection tasks - it contains Objects annotations for 7,895 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Files included: • Data file (Pew_February_2012) – Data from Pew Research, raw version available from http://www.pewinternet.org/datasets/february-2012-search-social-networks-and-politics/ • Syntax file (Clean syntax) – syntax for creation of all variables and all analysis.

nthakur-backup/bge-retrieval-data-ivf-passage-pruning-fixed-200K dataset hosted on Hugging Face and contributed by the HF Datasets community

Wikidata Thematic Subgraph Selection

These datasets have been designed to train and evaluate algorithms to select thematic subgraphs of interest in a large knowledge graph from seed entities of interest. Specifically, we consider Wikidata. Given a set of seed QIDs of interest, a graph expansion is performed following P31, P279, and (-)P279 edges. Traversed classes that thematically deviates from seed QIDs of interest should be pruned. Datasets thus consist of classes reached from seed QIDs that are labeled as "to prune" or "to keep".

Available datasets

Each dataset folder contains

• datasetX.csv: a CSV file containing one seed QID per line (not the complete URL, just the QID). This CSV file has no header.
• datasetX_labels.csv: a CSV file containing one seed QID per line and its label (not the complete URL, just the QID)
• datasetX_gold_decisions.csv: a CSV file with seed QIDs, reached QIDs, and the labeled decision (1: keep, 0: prune)
• datasetX_Y_folds.pkl: folds to train and test models based on the labeled decisions

dataset1-2 consists of using dataset1 for training and dataset2 for testing.

License

Datasets are available under the CC BY-NC license.