Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

Data acquisition and processing for cryo-electron tomography can be a significant bottleneck for users. To simplify and streamline the cryo-ET workflow, Tomo Live, an on-the-fly solution that automates the alignment and reconstruction of tilt-series data, enabling real-time data-quality assessment, has been developed. Through the integration of Tomo Live into the data-acquisition workflow for cryo-ET, motion correction is performed directly after each of the acquired tilt angles. Immediately after the tilt-series acquisition has completed, an unattended tilt-series alignment and reconstruction into a 3D volume is performed. The results are displayed in real time in a dedicated remote web platform that runs on the microscope hardware. Through this web platform, users can review the acquired data (aligned stack and 3D volume) and several quality metrics that are obtained during the alignment and reconstruction process. These quality metrics can be used for fast feedback for subsequent acquisitions to save time. Parameters such as Alignment Accuracy, Deleted Tilts and Tilt Axis Correction Angle are visualized as graphs and can be used as filters to export only the best tomograms (raw data, reconstruction and intermediate data) for further processing. Here, the Tomo Live algorithms and workflow are described and representative results on several biological samples are presented. The Tomo Live workflow is accessible to both expert and non-expert users, making it a valuable tool for the continued advancement of structural biology, cell biology and histology.

Celebrating 25 years of helping organizations with their Data Quality Challenges

Since bad data can adversely affect the business, it is crucial to learn proactive measures to combat and treat it at its source

With better address data, the city added more than 300 missing addresses to the Census file provided by the federal government

How to use LinkedIn Ads’ new company targeting options to boost B2B lead generation
LinkedIn (client) recently rolled out additional targeting options for advertisers, allowing LinkedIn Ad users access to new Company Category B2B data comprised of Forbes, Fortune and platform data, along with the addition of growth rate targeting information. Search Engine Land

Report: Majority of B2B Marketers to Continue Investment in Data Quality in 2020
75 percent of B2B marketers plan to up their investment in data quality during 2020, while 90 percent said they view such investment leading to improved marketing and sales performance — two of the numerous findings of interest to digital marketers contained in recently-released Dun & Bradstreet report data. Chief Marketer

How COVID-19 Is Impacting Marketing Budgets at Enterprise Companies
B2B marketers expect to shift investment primarily to virtual events (78%), web content (72%), webinars (67%) and social media (66%) because of the pandemic, according to recently-released April enterprise-level company survey data. MarketingProfs

How COVID-19 has changed social media engagement [Report]
Sprout Social’s new pandemic-era data shows that LinkedIn posts perform the best on Wednesdays at 3pm, Thursdays from 9-10am, and Friday from 11am to noon, and that the media and entertainment industry has been publishing almost 9 more posts daily, according to new social media engagement data on interest to marketers. Sprout Social

Twitter Publishes New Data on Video and Ad Content Performance During COVID-19
Twitter increased its monetizable daily active users (mDAUs) by 23 percent during the quarter, and saw video view rates that rose by 5.5 percent, two of the findings in newly-released brand COVID-19 trend data. Social Media Today

Facebook Adds New 'Animate' Option to Add Motion to Still Images in Facebook Stories
Facebook has released new zoom, pan and other animation modes that bring marketers a variety of additional Facebook Stories options, and has also begun testing several new mood-based content reaction options, the social media giant recently announced. Social Media Today

YouTube Influencer Engagement Rate Benchmarks: What Are Good Rates?
Various YouTube channel categories sport a wide range of differing engagement benchmarks, according to recently-released YouTube influencer engagement rate report data, which also reveals that micro-influencers on the video platform can often achieve high engagement marks. MarketingCharts

LinkedIn Publishes Data on Latest Content Engagement Trends on the Platform
LinkedIn has released new content trend engagement data, including a breakdown by global regions that shows what the platform’s audience is looking for and engaging with, with pandemic-related content having seen some of the biggest increases in quantity, the firm announced. Social Media Today

Coronavirus reshapes consumer habits, creating 4 new segments, report finds
25 percent of consumers said they would pay more to buy from trusted brands and 23 percent from ethical brands — two of numerous findings of interest to digital marketers in newly-released Ernst & Young pandemic marketing report data. Marketing Dive

Facebook Outlines a Range of New Video Tools, Including Messenger Rooms for Group Video Hangouts
Facebook recently announced a variety of video-related updates to its numerous social communications properties, including a change which will allow up to 8 people to have WhatsApp video calls, while Messenger video received new virtual background options, among several other video feature updates. Social Media Today

ON THE LIGHTER SIDE:

A lighthearted look at our brand promise by Marketoonist Tom Fishburne — Marketoonist

Chiquita lets Spotify users unlock music playlists, branded prizes — Mobile Marketer

TOPRANK MARKETING & CLIENTS IN THE NEWS:

• Lee Odden — 10 Expert Tips for Marketing During a Crisis — Oracle (client)
• Lee Odden — 4 takeaways for content marketers in the time of COVID-19 — Search Engine Land
• Lee Odden — 5 Hours of Content Marketing - Break Free of Boring B2B with Influential Content Experiences — SEMrush

Have you got your own top B2B content marketing or digital advertising stories from the past week of news? Please let us know in the comments below.

Thanks for taking time to join us, and we hope you will join us again next Friday for more of the most relevant B2B and digital marketing industry news. In the meantime, you can follow us at @toprank on Twitter for even more timely daily news. Also, don't miss the full video summary on our TopRank Marketing TV YouTube Channel.

The post B2B Marketing News: B2B Marketers Invest in Data Quality, Top Times to Post During Pandemic, LinkedIn’s Engagement Trends, & Facebook’s Video Updates appeared first on Online Marketing Blog - TopRank®.