IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics

TAFE is a research publication covering circuits and systems, applied to the Agri-food chain.

03/11/2025

🌾 Most Cited TAFE Paper

Congratulations to the authors of this month’s most-cited paper in IEEE Transactions on AgriFood Electronics (TAFE)!

🔹 MHAFF: Multihead Attention Feature Fusion of CNN and Transformer for Cattle Identification
Rabin Dulal, Lihong Zheng, Muhammad Ashad Kabir
https://loom.ly/rLKzek4

This study demonstrates how hybrid deep learning models can advance precision livestock management — an important step toward smarter, more efficient agriculture.

📚 If you’re researching in agricultural AI or smart farming, consider building on this innovative work by citing it in your own research.

31/10/2025

🔎 Discover insights that can advance your research. Explore past papers from IEEE Transactions on Artificial Intelligence for Engineering (TAFE) on IEEE Xplore and see how the latest developments in AI for engineering can inspire your next breakthrough.

🔗 Browse the archive at the link in the comments.

30/10/2025

Explore the most-read papers from IEEE Transactions on Artificial Intelligence for Engineering (TAFE) in September 2025!

These standout studies showcase innovative uses of AI and intelligent systems across agriculture and environmental sensing:

🌱 Plant Microbial Fuel Cells: Energy Sources and Biosensors for Battery-Free Smart Agriculture
Maria Doglioni; Matteo Nardello; Davide Brunelli
🔗 Read on IEEE Xplore: https://loom.ly/kUWI5X0

🚜 Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements
Sean J. Harkin; Tomás Crotty; John Warren; Conor Shanahan; Edward Jones; Martin Glavin; Dallan Byrne
🔗 Read on IEEE Xplore: https://loom.ly/ve_Dzf0

🪲 Beyond the Naked Eye: Computer Vision for Detecting Brown Marmorated Stink Bug and Its Punctures
Lennart Almstedt; Francesco Betti Sorbelli; Bas Boom; Rosalba Calvini; Elena Costi; Alexandru Dinca; Veronica Ferrari; Daniele Giannetti; Loretta Ichim; Amin Kargar; Catalin Lazar; Lara Maistrello; Alfredo Navarra; David Niederprüm; Peter Offermans; Brendan O'Flynn; Lorenzo Palazzetti; Niccolò Patelli; Cristina M. Pinotti; Dan Popescu; Aravind K. Rangarajan; Liviu Serghei; Alessandro Ulrici; Lars Wolf; Dimitrios Zorbas; Leonard Zurek
🔗 Read on IEEE Xplore: https://loom.ly/MUZcPVw

💡 Discover how TAFE research is driving innovation at the intersection of AI and engineering.
Explore more in the TAFE archive: https://loom.ly/xw9uto4

29/10/2025

🗻 Elevate your research with our RSS feed feature for IEEE TAFE!

With the TAFE RSS feed, you can effortlessly access the latest from the IEEE Transactions on AgriFood Electronics

🔗 View it here today👉 https://loom.ly/GDNJ2Sk

27/10/2025

Discover the minds shaping the future of AgriFood Electronics! 🌾🤖 Our publication explores industry research, global trends, and the stories of the engineers behind it all.

If you're passionate about sustainable agriculture, cutting-edge sensors, and innovative tech, check out our published articles here👉 https://loom.ly/xw9uto4

24/10/2025

➿ Stay in the loop with the latest from the IEEE Transactions on AgriFood Electronics (TAFE).

🔗 Visit this link and click "Add title to my alerts" on the upper right side of the page to sign up for alerts and never miss an issue👉 https://loom.ly/xw9uto4

22/10/2025

🌾 Associate Editor Highlight | IEEE Transactions on AgriFood Electronics (TAFE) 🌾

Meet Dr. Zheng Liu, Professor at the University of British Columbia – Okanagan and Associate Editor of IEEE TAFE.

Dr. Liu brings extensive expertise in predictive maintenance, data fusion, computer vision, machine learning, smart sensors, and industrial IoT—with research applications spanning AgTech, digital twins, and non-destructive inspection.

He holds doctorates from Kyoto University and the University of Ottawa, and is a Senior Member of IEEE, Fellow of SPIE, and a licensed Professional Engineer in British Columbia and Ontario.

In addition to his role with IEEE TAFE, Dr. Liu serves on the editorial boards of leading journals including IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Mechatronics, and Information Fusion (Elsevier).

We’re proud to have Dr. Liu contributing his expertise to advance innovation in AgriFood electronics!

21/10/2025

Whether you're an academic, researcher, engineer, or just someone curious about the latest advancements in this exciting field, following TAFE on social media will keep you up-to-date with the latest breakthroughs and innovations.

Stay connected
🔹Linkedin: https://loom.ly/fndSheg
🔹Facebook: https://loom.ly/PyeMBGc
🔹X (formerly Twitter): https://loom.ly/jgGTWeE

20/10/2025

📄 Become a Volunteer TAFE Reviewer!

Are you interested in the application of circuits and systems to the AgriFood chain of value? Help shape the future of research by reviewing manuscripts for IEEE Transactions on AgriFood Electronics (TAFE).

🔗Sign up today: https://loom.ly/R9h3w0k

17/10/2025

The IEEE Transactions on AgriFood Electronics (TAFE) is paving the way for a smarter, more sustainable food system.

TAFE explores cutting-edge research in:

📌 Precision Agriculture
📌 Smart Livestock Farming
📌 Climate Change and Global Warming Facing
📌 Reduction of Pesticides
📌 Optimization of Resources
📌 Better Food Production
📌 Food Safety
📌 Food Processing

🔗 Learn more about TAFE and its topics here: https://loom.ly/gSKYK3o

16/10/2025

🌾 Paper Highlight: “Precision Fertilization via Spatio-temporal Tensor Multi-task Learning and One-Shot Learning”
Vol. 3, Issue 1

Efficient fertilizer use is critical for sustainable agriculture—but limited, unstructured farm data often hinder machine learning applications. This study introduces a novel approach combining multi-task and one-shot learning to overcome data scarcity and improve precision fertilization decisions.

Using real agricultural data from nine winter wheat farms in the UK, the authors develop a spatio-temporal tensor model that captures environmental and soil nutrient patterns to accurately predict fertilization timing and quantity.

The results show greater accuracy and stability compared to standard regression methods, demonstrating the potential of AI-driven solutions for sustainable crop management.

🔗 Access the paper at the link in the comments.

10/10/2025

❓ Author FAQ: How do I choose the right IEEE journal for my submission?

✅ Use the IEEE Publication Recommender Tool to get suggestions based on your manuscript.
✅ Search IEEE Xplore to see where similar research is being published.
✅ Always review the journal’s Aims & Scope to ensure a good fit.

🔗 Learn more: https://loom.ly/KuphyYc

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