Improving Indian agriculture focus of new Arkansas Clean Plant Center partnership
Global ag biosecurity, decreased pesticide use, sustainability, factors of project
University of Arkansas System Division of Agriculture
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Priya Ranjan, left, joint secretary of the Indian Department of Agriculture and Farmers Welfare, and Jean-François Meullenet, senior associate vice president for agriculture-research and director of the Arkansas Agricultural Experiment Station, sign an agreement Nov. 18, 2025, to collaborate through the Arkansas Clean Plant Center, which aims to help implement a clean plant program in India.
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By John Lovett
University of Arkansas System Division of Agriculture
Arkansas Agricultural Experiment Station
FAYETTEVILLE, Ark. — The University of Arkansas System Division of Agriculture recently entered a five-year agreement with the Indian National Horticulture Board and Ministry of Agriculture and Farmers Welfare to help farmers in India improve agricultural production by limiting spread of pathogens.
India is one of the world’s largest producers of fruits and vegetables, but a lack of disease-free propagation material has limited the yield potential of the nation’s mostly small-scale farmers.
Ioannis Tzanetakis, director of the Arkansas Clean Plant Center and professor of plant virology for the Arkansas Agricultural Experiment Station, has been working on the Indian Clean Plant Program for almost three years. The project’s goal is to establish nine clean plant centers in India.
The Arkansas Clean Plant Center is a part of the experiment station, the research arm of the Division of Agriculture. As part of the memorandum of cooperation signed Nov. 18 between the Indian government agencies and the University of Arkansas Board of Trustees on behalf of the Division of Agriculture, the Arkansas Clean Plant Center in Fayetteville will host scientists from India for the training needed to develop the Indian Clean Plant Program.
The partnership is focused on training advanced diagnostic methods, virus elimination and implementation of science-based certification systems, Tzanetakis noted. Indian scientists will work with the Arkansas Clean Plant Center’s team to gain practical experience in clean plant operations, from testing to greenhouse management and disease elimination protocols.
“I’m proud of this collaboration with India’s Ministry of Agriculture and Farmers Welfare, which represents a major step forward in our shared commitment to plant health and sustainable agriculture,” Tzanetakis said. “These exchanges will not only strengthen our respective programs but also build lasting partnerships that enhance global agricultural biosecurity, something that I have worked on in the better part of my career.”
Jean-François Meullenet, senior associate vice president for agriculture-research and director of the Arkansas Agricultural Experiment Station, said the partnership fits into the university system’s land-grant mission of performing public service by enhancing global agricultural biosecurity for one of the United States’s largest trading partners. The project will help decrease pesticide use and improve both environmental and economic sustainability for India’s mostly small-scale farmers.
Expanding international relationships is also a key part of the Division of Agriculture’s strategic plan.
“This partnership strengthens a long-lasting relationship with India and the United States,” Meullenet said. “We look forward to the many positive benefits that will come from the Indian Clean Plant Program.”
Global collaboration for virus elimination
Priya Ranjan, joint secretary for the Indian government’s Department of Agriculture and Farmer Welfare, said Tzanetakis offers a deep understanding of the U.S. National Clean Plant Network since he has been part of it since it was established in 2008.
“He knows where things can go wrong,” Ranjan said of Tzanetakis. “With his experience, his insights, and his learnings, we intend not to replicate them. It is going to be an ongoing collaboration wherein we’ll be getting all the kind of support from Ioannis and other partners across the world to build a very robust program.”
Ranjan said most of the farms in India are about 2 acres, and the nation’s horticultural output is around 365 million metric tons per year.
“But we have our challenges, and I think one of the main challenges will be addressed through the Clean Plant Program when we clean the majority of the economically important horticulture commodities that we have in India,” said Ranjan, who is also the managing director of the National Horticulture Board, the commercial arm for promotion of horticulture in India.
V.B. Patel, assistant director general of the Horticultural Science Division for the Indian Council of Agricultural Research, said the first crop they will focus on eliminating pathogens from is grapes.
If they are successful with eliminating pathogens in grapes, Patel said crops that the Indian Clean Plant Program will further focus on include pomegranates, apples, pears and walnuts, as well as tropical fruits like mangos, avocados and bananas.
In their cost-benefit analysis, Ranjan said they found that across the world there are many examples where yield depressions have happened because of viral infestations, including citrus in the United States.
“If we are able to eliminate these pathogens to a substantial level, so that they are not economically going to hamper the productivity and the incomes of the farmers, I think we’ll be doing a great service,” Ranjan said.
In addition to training, the agreement establishes opportunities for developing collaborative programs such as exchange programs for information, students, faculty, researchers and administrators.
To learn more about the Division of Agriculture research, visit the Arkansas Agricultural Experiment Station website. Follow us on X at @ArkAgResearch, subscribe to the Food, Farms and Forests podcast and sign up for our monthly newsletter, the Arkansas Agricultural Research Report. To learn more about the Division of Agriculture, visit uada.edu. Follow us on X at @AgInArk. To learn about extension programs in Arkansas, contact your local Cooperative Extension Service agent or visit uaex.uada.edu.
About the Division of Agriculture
The University of Arkansas System Division of Agriculture’s mission is to strengthen agriculture, communities, and families by connecting trusted research to the adoption of best practices. Through the Agricultural Experiment Station and the Cooperative Extension Service, the Division of Agriculture conducts research and extension work within the nation’s historic land grant education system.
The Division of Agriculture is one of 20 entities within the University of Arkansas System. It has offices in all 75 counties in Arkansas and faculty on three system campuses.
Pursuant to 7 CFR § 15.3, the University of Arkansas System Division of Agriculture offers all its Extension and Research programs and services (including employment) without regard to race, color, sex, national origin, religion, age, disability, marital or veteran status, genetic information, sexual preference, pregnancy or any other legally protected status, and is an equal opportunity institution.
Priya Ranjan, left, joint secretary of the Indian Department of Agriculture and Farmers Welfare, and Jean-François Meullenet, senior associate vice president for agriculture-research and director of the Arkansas Agricultural Experiment Station, shake hands after signing an agreement to collaborate through the Arkansas Clean Plant Center, which aims to help implement a clean plant program in India.
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U of A System Division of Agriculture photo
How to improve the fault diagnosis accuracy of puffing machines?
Higher Education Press
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view moreCredit: HIGHER EDUCATON PRESS
In the development of modern animal husbandry, the feed industry serves as a crucial material foundation, and extrusion puffing technology has become one of the mainstream feed processing technologies due to its unique advantages. However, the intelligence level of puffing machines currently available on the market is relatively low, making them prone to faults such as cavity blockage and cutter wear during operation. In the event of severe blockage, manual dismantling and cleaning of the expansion cavity are required. The high temperature of the cavity may cause injuries to operators, posing significant safety risks. How to develop a highly intelligent, stable and reliable fault diagnosis system for puffing machines to reduce the risks of manual troubleshooting and improve production efficiency?
Associate Professors Yongjian Wang and Xuebin Feng from the College of Engineering, Nanjing Agricultural University, proposed a fault diagnosis system for puffing machines based on a Bayesian-optimized convolutional neural network and multi-head attention mechanism (BO-CNN-MHA). By integrating multi-source information fusion technology, the system combines monitoring data such as temperature, noise, main motor current and vibration signals of key components to construct an intelligent diagnosis model capable of capturing both local and global features. The relevant research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025634).
The core of the system lies in the collaborative analysis of multi-source sensor signals. The research team deployed 7 types of monitoring sensors on the puffing machine, including PT100 temperature sensors, SHT20 feed temperature and humidity sensors, SLS132R-25 ambient temperature and humidity sensors, vibration sensors, noise sensors, current sensors and weighing sensors, enabling comprehensive perception of the equipment’s operating status. After data collection by the Raspberry Pi 4B processor, the model’s hyperparameters are optimized using the Bayesian optimization algorithm, which are then input into a deep learning framework integrating convolutional neural networks (CNN) and multi-head attention mechanism (MHA).
CNN is responsible for extracting local features from the data, such as high-frequency components of vibration signals and temperature change trends. MHA captures global correlations between different features through parallel computation of multiple attention heads, such as the combined impact of cavity temperature and feed humidity on blockage faults. This structural design addresses the limitations of traditional fault diagnosis methods that rely on single-source signals and incomplete feature extraction, enhancing the model’s ability to recognize complex fault patterns.
To verify the system’s performance, the research team collected 4760 sets of puffing machine operating data from December 2023 to January 2024, covering normal operation and 7 types of fault states. Key influencing factors such as cavity temperature, feed humidity and ambient temperature were identified through feature correlation analysis and SHAP value importance evaluation, and the sensor combination was optimized.
Experimental results show that the BO-CNN-MHA model achieved an overall accuracy of 99.4% on the test set, with a 100% recognition accuracy for states such as normal operation, slight blockage and inlet clogged. In practical working condition verification, the system achieved an average recognition rate of 98.8% for 1645 sets of balanced sampling data, including 99.1% for inlet clogged, and over 98% for both screw loosening and severe cutter wear. This performance outperforms traditional ANN, BP neural network and single CNN models, meeting the real-time diagnosis requirements in actual production of puffing machines.
The promotion and application of this system will significantly reduce the manual reliance on puffing machine fault troubleshooting and minimize safety accidents caused by the dismantling of high-temperature cavities. Compared with existing technologies such as the continuous lubrication system from Taiwan’s IDAH Company and the downtime analysis tool from Switzerland’s Bühler Company, this system achieves accurate classification and early warning of fault types through multi-source data fusion and AI algorithms, providing an intelligent solution for feed processing enterprises.
Journal
Frontiers of Agricultural Science and Engineering
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Fault diagnosis system for puffing machine with Bayesian-optimized convolutional neural network and multi-head attention based on multi-source fusion
Article Publication Date
15-Dec-2025
