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Chandigarh University researchers use Artificial Intelligence to develop Model for predicting Accurate Crop Yield; Innovation to benefit Indian Farmers

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Researchers have leveraged Climate Data and Satellite Technology for developing the predictive model

CHANDIGARH, India, June 27, 2026 /PRNewswire/ — In a significant advancement for smart agriculture and precision farming, researchers at Chandigarh University have developed an Artificial Intelligence (AI)-powered Transformer Model that is capable of accurately predicting crop yields using satellite imagery, climate data and historical agricultural records.

The innovation would be instrumental in empowering farmers, policymakers and agricultural agencies to make informed decisions while strengthening food security and advancing resilient farm management.

The research, led by Kusum Lata, Assistant Professor Department of Computer Science Engineering, Chandigarh University, Dr Navneet Kaur Professor Department of CSE and Dr Simrandeep Singh Professor from University Centre of Research and Development at Chandigarh University that focuses on improving crop yield forecasting in Punjab’s agricultural heartland. The study, recently presented at the 2026 International Conference on Signal Processing and Electronics Design (ICSPED) at Chandigarh College of Engineering and Technology, Chandigarh that introduces a lightweight transformer-based system that leverages multi-source data to estimate crop production before harvest with greater accuracy and lower computational costs.

Notably, the accurate crop yield prediction has become increasingly important as farmers face growing challenges from climate variability, changing weather patterns and rising food demand. Traditional field surveys are often time-consuming, labour-intensive and limited in scale. The Chandigarh University researchers sought to overcome these limitations by integrating advanced AI techniques with real-time Earth observation data.

Kusum Lata, Assistant Professor Department of Computer Science Engineering at CU said, “The transformer model utilizes data from Sentinel-1 and Sentinel-2 satellites which are advanced Earth observation satellites operated by the European Space Agency (ESA) to continuously monitor agricultural fields and provide information on crop growth, vegetation health, soil moisture and field conditions. The satellite observations are combined with climatic variables such as rainfall, temperature and soil moisture, along with historical crop production records, creating a comprehensive picture of crop performance throughout the growing season.”

Kusum added, “Unlike conventional machine learning models, the newly developed lightweight transformer can identify critical crop growth stages and learn complex temporal patterns that influence final yields. We have designed the model to deliver high predictive performance that require fewer computational resources, making it suitable for practical deployment in large-scale agricultural monitoring systems.”

“The model was evaluated on four major crops cultivated in Ludhiana district, namely paddy, maize, moong and sugarcane, using data collected between 2019 and 2023. Experimental results demonstrated that the transformer model outperformed widely used approaches such as Random Forest and Long Short-Term Memory (LSTM) models, indicating stronger agreement between predicted and actual yields. The framework also recorded lower prediction errors and improved computational efficiency.

Kusum has worked as a Junior Research Fellow in the Agriculture and Crop Monitoring Division at Punjab Remote Sensing Centre (PRSC), PAU Ludhiana, contributed to geospatial research projects, including crop residue burning analysis using remote sensing and geospatial mapping techniques.

The study further revealed that the lightweight architecture requires nearly 40 percent fewer parameters than conventional transformer models while delivering faster and accurate predictions. This level of efficiency makes this automatic framework suitable for near real-time agricultural applications, including regional crop monitoring, production forecasting and early warning systems.

According to the researchers, the ability to accurately forecast crop yields before harvest can have significant implications for farmers and governments alike. Reliable forecasts can support agricultural planning, optimize resource allocation, strengthen crop insurance mechanisms and improve market management strategies. In a state like Punjab, where agriculture plays a central role in the economy, such technologies can contribute to more resilient and sustainable farming systems.

The researchers also shared that one of the key strengths of the model lies in its ability to combine multiple sources of information into a single predictive model. By integrating satellite-derived observations with climatic and historical datasets, the system captures the complex interactions that influence crop productivity and provides a more robust understanding of agricultural outcomes.

The future developments will also focus on enabling near real-time forecasting through cloud-based platforms, paving the way for broader adoption of AI-driven decision support systems in agriculture, added the Chandigarh University researchers.

About Chandigarh University

Chandigarh University is a NAAC A+ Grade University and QS World Ranked University. This autonomous educational institution is approved by UGC and is located near Chandigarh in the state of Punjab. It is the youngest university in India and the only private university in Punjab to be honoured with A+ Grade by NAAC (National Assessment and Accreditation Council). CU offers more than 109 UG and PG programs in the field of engineering, management, pharmacy, law, architecture, journalism, animation, hotel management, commerce, and others. It has been awarded as The University with Best Placements by WCRC.

Website address: https://www.cuchd.in/

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Harlem Children’s Zone Acquires FinTech Platform, Goalsetter, to Accelerate Universal Economic Mobility

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Acquisition combines HCZ’s proven model with Goalsetter’s innovative financial education technology to expand access and impact at scale

NEW YORK, Oct. 22, 2026 /PRNewswire/ — Today, Harlem Children’s Zone (HCZ), one of the nation’s leading anti-poverty organizations, announced the acquisition of Goalsetter, a financial technology platform that delivers best-in-class, engaging, technology-enabled learning experiences for people seeking to enhance their financial know-how, capabilities, and confidence.   

The acquisition marks a significant step in HCZ’s Wealth Builds–Universal Economic Mobility (WB-UEM) strategy, a national framework designed to help children and families build long-term economic security through cradle-to-career supports, financial education and experiences, and direct access to capital via a Child Capital Stack of asset boosts.

“We know that economic mobility is a two-generation challenge,” says Harlem Children’s Zone CEO Kwame Owusu-Kesse. “With this acquisition, we are solving for a critical component of Universal Economic Mobility by melding a proven financial education platform and HCZ’s decades of experience providing people with the knowledge and tools to build strong futures for themselves and their families. This is more than an investment in financial education. It is an investment in critical national infrastructure for economic mobility.”

Harlem Children’s Zone has decades of experience driving outcomes at scale, beginning as a one-block pilot, expanding to over 100 blocks in Central Harlem, and now working with over 70 communities across the United States. HCZ seeks to use its learnings, decades of experience driving outcomes at scale, and renowned best practices to provide pathways to economic mobility for all of America’s children and families.

“Goalsetter was built on the belief that everyone should have access to the financial knowledge and tools that can shape their future and livelihood,” said Tanya Van Court, Chief FinTech Officer at Harlem Children’s Zone and Founder of Goalsetter. “Joining Harlem Children’s Zone gives us an extraordinary opportunity to build on that work. Together, we can continue enhancing the platform, expand access for learners of all ages and walks of life, and demonstrate what is possible when mission, innovation, and technology come together to serve the most people.”

Goalsetter creates opportunities to deliver high-quality, developmentally appropriate and culturally relevant financial education with extensive reach,  including to low-income and rural communities that have historically had limited access to these resources. Its current offerings include Goalsetter Classroom, which provides standards-aligned financial education curriculum for middle and high school students, and MoneyLingo, a mobile-first financial education experience for college-aged students and adults in the workforce, covering practical topics such as saving, investing, credit, debt, and financial decision-making.                                                         

“This acquisition sends an important signal to the field,” said Owusu-Kesse. “Social impact organizations should not have to choose between mission and scale. By bringing a proven technology platform into HCZ, we’re demonstrating how non-profit organizations can be centers of business excellence, using vehicles of innovation, AI, and technology to create efficient and effective pathways to scale and impact. This acquisition strengthens our in-house capacity to build, innovate, and continuously improve outcomes.”

The organization plans to build upon Goalsetter’s existing offerings by exploring financial education products for younger learners and creating opportunities to establish a continuous financial learning journey from early childhood through adulthood.

“This is ultimately about extending opportunity to all,” added Owusu-Kesse. “By connecting financial education, technology, family engagement, and access to assets, we can help create more accessible pathways to economic mobility.”

Services to current Goalsetter clients will be unaffected by this acquisition.

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SOURCE Harlem Children’s Zone

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Common Ground in the Battlegrounds? Surveys Reveal Widespread Bipartisan Agreement in 11 Competitive States and 28 Congressional Districts, as Well as Nationally

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Bipartisan Majorities Say If Congress Were to Listen to the People More, They Would More Likely Find Common Ground

COLLEGE PARK, Md., Oct. 22, 2026 /PRNewswire/ — The Program for Public Consultation (PPC) conducted extensive surveys in competitive states and congressional districts, as well as nationally, revealing a remarkable level of bipartisan common ground.

Majorities of both Democrats and Republicans agreed on: 

106 policies nationally 82 policies in all states and districts surveyed101 policies in at least two-thirds of all the states and districts surveyed

Over 40,000 Americans were surveyed in two waves with representative samples of 600-2,016 in each of the 11 competitive states surveyed, and 400-592 in each of the 28 competitive congressional districts surveyed, and 1,200-2,509 nationwide.

Nearly all policy questions were based on congressional legislation or executive branch proposals. 

Fourteen policy topics were covered: Artificial Intelligence, Housing, Healthcare, Low-Income Assistance, Families and Children, Social Security, Immigration, Abortion and Birth Control, Energy and Environment, Federal Taxes, Campaign Finance, Government Reform, Criminal Justice and America’s Role in the World. For every topic, most policies had bipartisan agreement across every battleground surveyed.

The policy questions used in these national and battleground surveys were chosen because they elicited bipartisan support in previous in-depth national surveys conducted by PPC, in which respondents received briefings on the topic and evaluated pro and con arguments for the proposed policy.

Steven Kull, director of PPC, comments, “What is striking here is that these surveys revealed widespread bipartisan agreement on over a dozen key issues that Congress has failed to resolve, from costs of healthcare, to risks of artificial intelligence, to Social Security’s impending insolvency, and many more. Clearly, there is far more common ground among the American people than there is in Congress.”

Americans Think Public Opinion Should Have More Influence
Respondents were also asked about the role of public opinion in policymaking. Across the battlegrounds, on average, respondents estimated that federally elected officials make the same decisions that the majority of Americans would make just 35% of the time.

Large majorities believe polarization in government comes not from the American people but from elected officials, and that more influence for the people would help break the logjam in Congress (Democrats 62 to 88%, Republicans 66 to 89%).

Perhaps most germane, overwhelming majorities of Democrats and Republicans in the battlegrounds believe that if members of Congress were more influenced by the people, they would be more likely to find common ground (Democrats 82 to 97%, Republicans 80 to 95%), and better serve the common good (Democrats 77 to 94%, Republicans 79 to 94%).

Steven Kull adds, “On the basis of this study, it does appear that the public is correct that if the people had more influence, Congress would be more likely to find common ground, and thus  function better.”

Americans also say that public opinion should play a substantial role in news reporting about policy. Over two-thirds say news reports on policy matters should include public opinion data most of the time or always when reliable information is available (Democrats 71 to 88%, Republicans 67 to 89%). Similarly, when Congress is about to vote on a law, large numbers say it is very or extremely important for news outlets to report whether a majority of Americans favor or oppose it (Democrats 65 to 84%, Republicans 60 to 85%).

MORE INFORMATION

Battlegrounds Common Ground SheetIndividual State and District Results, Questionnaires and MethodologiesNational Questionnaire with Toplines, Crosstabs and Methodology

About the Surveys
The state and district surveys used for this analysis were fielded July 21 – August 17, 2026, to a total of 19,949 adults, and May 27 – July 1, 2026, to 19,564 adults. They included 600-2016 in each of the 11 states, and 400-592 in each of the 28 congressional districts. Response rates ranged from 7.1 to 9.0%. Confidence intervals range from +/- 2.9 to 8.1%, and maximum weights range from 1.9 to 10.0.

The national surveys were fielded February 11-26, 2026, with 2,509 adults, and March 11-19, 2026, with 1,200 adults. The response rates were 7.6% and 8.3%, respectively. The confidence interval is +/- 2.9% for both surveys, and the maximum weights are 2.5 and 1.9.

The sample was obtained from multiple online opt-in panels, including Cint, Dynata and Prodege. Sample collection and quality control were managed by QuantifyAI under the direction of the Program for Public Consultation. The surveys were offered in both English and Spanish.

The samples were pre-stratified and weighted by age, race, ethnicity, gender, education, household income, marital status, and home ownership to match the general adult population. The national sample was further weighted by Census region. The distribution of partisan affiliation was within expectations, based on voter registration data for non-leaners and/or Cook’s Partisan Voting Index for leaners, or weighted to reflect those benchmarks.

About the Common Ground in the Battlegrounds? Survey Series
This effort aims to identify bipartisan common ground in competitive states and districts across the nation. The project seeks to educate the public, policymakers and candidates about the findings; and provide journalists with independent, nonpartisan research that brings the public into the conversation about public policy.

In addition to PPC, project partners include the Civic Innovation Center at the University of Maryland (CivIC) and Voice of the People (VOP), a national nonpartisan organization seeking to give the public a greater voice in government. None of these entities advocate for policy positions or endorse candidates.

States surveyed: Alaska; Georgia; Iowa; Maine; Michigan; Minnesota; Montana; Nebraska; New Hampshire; North Carolina; Ohio; and Texas.

Congressional Districts surveyed: Arizona’s 1st and 6th; Colorado’s 8th; Iowa’s 1st, 2nd, 3rd and 4th; Maine’s 2nd; Michigan’s 4th, 7th, 8th and 10th; Montana’s 1st; Nebraska’s 2nd; Nevada’s 3rd; New Jersey’s 7th and 9th; New Mexico’s 2nd; New York’s 3rd, 4th, 17th and 19th; Oregon’s 5th; Pennsylvania’s 1st, 7th, 8th and 10th; and Washington’s 3rd.

The survey series excluded several competitive districts because they were in states that had recently been redistricted and thus had no Census data yet to use for demographic benchmarks.

Program for Public Consultation
The Program for Public Consultation (PPC), affiliated with the University of Maryland’s School of Public Policy, seeks to improve democratic governance by consulting the public and bringing the people’s views into the public discourse. It conducts surveys with representative samples on policies proposed by elected officials and shares its findings with government officials, the media, other academics, and the general public.

Civic Innovation Center
The Civic Innovation Center (CivIC) at the University of Maryland’s School of Public Policy works to strengthen the foundations of civic health. It contributes to important debates about civic education, civic engagement, and needed democratic reforms in the state of Maryland and nationwide, in partnership with other BIG 10 universities. It works to build the connective tissue that brings together students, scholars and practitioners to help elevate the UMD community’s contribution to our wider democracy.

Voice of the People
Voice of the People (VOP) is a nonpartisan organization working to re-anchor our democracy in its founding principles by giving ‘We the People’ a greater and more effective voice in government. It champions innovative methods and technology that enable the American people to play a role in the policymaking process and provide policymakers with a more accurate understanding of the views of their constituents.

Contact: Jonathan Casaverde, jonathan.casaverde@wardcirclestrategies.com,(786) 469-9422

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SOURCE Program for Public Consultation

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FDA grants 510(k) clearance for BoneEnhance, the first machine learning layer on RIVANNA’s Accuro XV platform

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Anatomy segmentation and three-dimensional skeletal visualization arrive with this clearance; an injury detection module for clinical decision support is in development on the platform roadmap

CHARLOTTESVILLE, Va., Oct. 22, 2026 /PRNewswire/ — RIVANNA®, developer of AI-enabled clinical decision-support solutions, today announced that the U.S. Food and Drug Administration (FDA) has granted 510(k) clearance for BoneEnhance®, a machine learning software module for the Accuro® XV diagnostic ultrasound system. The module segments bone and surrounding tissues and supports visualization of skeletal anatomy in three dimensions during image review.

The clearance moves the platform from acquisition toward interpretation. Accuro XV was cleared in April 2026 for B-mode imaging in musculoskeletal conventional and superficial applications. Its conformable three-dimensional probe automates large field-of-view image acquisition after initial patient positioning. BoneEnhance is the first machine learning software module cleared for the platform.

BoneEnhance identifies bone and surrounding tissues

Each automated acquisition creates a volumetric dataset for review. BoneEnhance identifies bone and surrounding tissues within that volume, then highlights them with color-coded overlays on the two-dimensional B-mode image. The original anatomical data remain unaltered and visible beneath the overlays. A three-dimensional indicator beside the image shows the scan orientation and the location of the displayed slice within the volume.

A platform milestone

The sequence is deliberate: decision support begins at acquisition. Accuro XV acquires the image; BoneEnhance identifies and labels the anatomy within it. Behind the integrated workflow stands a worldwide intellectual-property portfolio of more than 100 issued and pending patents across more than 20 patent families.

“We founded RIVANNA to solve a problem that cuts across care settings: decision-critical information sits outside the point-of-care workflow,” said Will Mauldin, PhD, Co-founder and CEO of RIVANNA. “BoneEnhance is where the stack begins to close that gap for musculoskeletal care: automated volumetric acquisition, now paired with cleared software-assisted review. This clearance is the foundation: the first machine learning layer, anchoring the decision-support capabilities now in development.”

Independently validated within a nationwide eight-site study

BoneEnhance was trained and validated within RIVANNA’s nationwide eight-site clinical study. Three sites contributed the training data; the other five provided the independent test set of 233 volumetric datasets, comprising 69,900 images from 173 subjects. None of the test data were used for training or tuning. Three board-certified radiologists established the reference labels, and per-volume label prediction accuracy, a measure of anatomical labeling, ranged from 95.1% to 100.0%.

Separate from the validation supporting this clearance, the platform’s automated acquisition carries peer-reviewed evidence: in a published study of 205 patients in the Journal of Emergency Medicine (DOI: 10.1016/j.jemermed.2025.11.011), operators with one hour of hands-on training acquired wrist and ankle images rated adequate for diagnostic interpretation in more than 90% of scans, with image quality comparable to physician-acquired scans.

“This clearance reflects the strength of the team RIVANNA has built across AI development, clinical affairs, and regulatory execution, and the commitment of the clinical sites that partnered with us to generate the evidence,” said Delphine Le Roux, PhD, PMP, Senior Director, Market Access and Strategic Partnerships at RIVANNA. “None of it happens without BARDA’s sustained support, which allowed us to do this the right way: rigorous, multi-site, and independent. The infrastructure, partnerships, and regulatory playbook we established for BoneEnhance are designed to carry forward. That execution engine is what positions us to pursue the next clearance with confidence.”

The next layer in development

The platform’s progression continues with an investigational musculoskeletal injury detection module. Where BoneEnhance labels the anatomy, the new module is being developed to assist clinicians in evaluating potential extremity fractures within acquired ultrasound images. The nationwide clinical study that trained and validated BoneEnhance, now expanded from eight to ten sites, is building the datasets used to train the module. Because the sites and data infrastructure were established once and are reused for each new module, every subsequent module builds on the program’s federal investment.

Federal funding acknowledgment

This project has been supported in whole or in part with federal funds from the U.S. Department of Health and Human Services; the Administration for Strategic Preparedness and Response (ASPR); Biomedical Advanced Research and Development Authority (BARDA), under contract number 75A50121C00035.

About Accuro XV

Accuro XV is a portable, small-footprint point-of-care volumetric ultrasound imaging system for musculoskeletal imaging. Its conformable three-dimensional probe automates large field-of-view B-mode image acquisition without ionizing radiation. BoneEnhance is a cleared machine learning software module available during review of Accuro XV volumetric image acquisitions. It segments bone and surrounding tissues and supports visualization of skeletal anatomy in three dimensions.

An investigational computer-aided musculoskeletal injury detection module is in development for Accuro XV. It is designed to assist clinicians in detecting extremity fractures from volumetric ultrasound and annotating soft-tissue signs associated with injury, and to support triage decisions, including referral for additional radiographic imaging.

Accuro XV is designed to provide a fracture-care layer at the point of triage, helping to streamline emergency department workflows, expedite evaluation of low-severity extremity injuries, and reduce reliance on X-ray referrals. The platform is designed to support just-in-time training to promote consistent use across clinical experience levels, advancing a shared mission to transform musculoskeletal injury care while supporting broader emergency preparedness objectives.

About RIVANNA

RIVANNA is a medical technology company developing clinical decision-support solutions powered by proprietary clinical datasets, AI models, and purpose-built imaging hardware. The company’s platform is designed to automate complex anatomical analysis at the point of care, support faster and more confident clinical decisions, reduce variability, and expand access to advanced capabilities. Its initial applications address interventional guidance and trauma triage, beginning with neuraxial procedures and musculoskeletal injury assessment. RIVANNA has established an FDA-cleared product portfolio and maintains a worldwide intellectual-property portfolio of more than 100 issued and pending patents. RIVANNA is headquartered in Charlottesville, Virginia, and operates an FDA-registered, ISO 13485:2016-certified manufacturing facility.

RIVANNA, Accuro, BoneEnhance, and the Rivanna Medical family of marks are registered and unregistered trademarks of Rivanna Medical, Inc. in the United States and other jurisdictions.

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SOURCE RIVANNA

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