The Impact of the EU’s AI Act and Data Act on Digital Farming Technologies DOI

Lucas Ramon Ciutat

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 218 - 229

Published: Nov. 15, 2024

Language: Английский

Analysing the potential of ChatGPT to support plant disease risk forecasting systems DOI Creative Commons
Roberta Calone, Elisabetta Raparelli, Sofia Bajocco

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100824 - 100824

Published: Feb. 1, 2025

Language: Английский

Citations

1
Knowledge assimilation: Implementing knowledge-guided agricultural large language model DOI
Jingchi Jiang, Lian Yan, Haifeng Liu

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113197 - 113197

Published: Feb. 1, 2025

Language: Английский

Citations

1
Climate-Resilient Agriculture: Leveraging Language Models for Mitigation and Adaptation DOI
Sathyanarayan Rao, Praveen Ranganath

Environmental earth sciences, Journal Year: 2025, Volume and Issue: unknown, P. 357 - 382

Published: Jan. 1, 2025

Language: Английский

Citations

1
The Role of Generative Artificial Intelligence in Digital Agri-Food DOI Creative Commons
Sakib Shahriar, Maria G. Corradini, Shayan Sharif

et al.

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101787 - 101787

Published: March 1, 2025

Language: Английский

Citations

1
Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection DOI Creative Commons
Hongyan Zhu, Chengzhi Lin,

G. Liu

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 24, 2024

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in yield quality caused by these problems. In recent years, remote sensing (RS) areas has been prevailed over unmanned aerial vehicle (UAV)-based applications. Herein, using methods such as keyword co-contribution analysis author co-occurrence bibliometrics, we found out hot-spots of this field. UAV platforms equipped with various types cameras other advanced sensors, combined artificial intelligence (AI) algorithms, especially deep learning (DL) were reviewed. Acknowledging critical role comprehending pests, along their defining traits, provided a concise overview indispensable foundational knowledge. Additionally, some widely used traditional machine (ML) algorithms presented performance results tabulated form comparison. Furthermore, summarized monitoring techniques DL introduced application prediction classification. Take it step further, newest most concerned applications large language model (LLM) vision (LVM) also mentioned herein. At end review, comprehensively discussed deficiencies existing research challenges be solved, well practical solutions suggestions near future.

Language: Английский

Citations

7
Evaluating AI-Generated Responses from Different Chatbots to Soil Science-Related Questions DOI Creative Commons
Javad Khanifar

Soil Advances, Journal Year: 2025, Volume and Issue: 3, P. 100034 - 100034

Published: Jan. 31, 2025

Language: Английский

Citations

0
Fields of the Future: Digital Transformation in Smart Agriculture with Large Language Models and Generative AI DOI
Tawseef Ayoub Shaikh,

Tabasum Rasool,

Waseem Ahmad Mir

et al.

Computer Standards & Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 104005 - 104005

Published: March 1, 2025

Language: Английский

Citations

0
Foundation Models in Agriculture: A Comprehensive Review DOI Creative Commons

Shanbing Yin,

Yongming Xi,

Xun Zhang

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(8), P. 847 - 847

Published: April 14, 2025

This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by need for efficient and intelligent decision support systems face growing global population climate change. It begins outlining development history FMs, including general FM training processes, application trends challenges, before focusing on Agricultural (AFMs). The examines diversity applications AFMs areas like crop classification, pest detection, image segmentation, delves into specific use cases such as agricultural knowledge question-answering, video analysis, support, robotics. Furthermore, it discusses challenges faced AFMs, data acquisition, efficiency, shift, practical challenges. Finally, future directions emphasizing multimodal applications, integrating across food sectors, decision-making systems, ultimately aiming to promote digitalization transformation agriculture.

Language: Английский

Citations

0
Generative AI for Smallholder Agricultural Advice in Sub-Saharan Africa DOI
Joyce Nakatumba‐Nabende, Ann Lisa Nabiryo, Peter Nabende

et al.

Oxford University Press eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract Smallholder farmers are prone to food insecurity due the devastating effects of viral crop diseases, pest outbreaks, and lack timely, targeted advice. Leveraging Large Language Models (LLMs) in agriculture offers significant potential bridge information gaps that smallholder face. This study discusses development an expert-reviewed agricultural question-answer dataset. We analysed responses from LLMs experts on crop- animal-related questions using relevancy, coherence, fluency metrics. Our results show GPT-4 outperforms other across these LLM-powered systems can act as virtual extension agents, assisting decision-making overcoming farming challenges.

Language: Английский

Citations

0
RicePest-DETR: A transformer-based model for accurately identifying small rice pest by end-to-end detection mechanism DOI
Jianqi Liu, Chenlian Zhou, Yujun Zhu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110373 - 110373

Published: April 17, 2025

Language: Английский

Citations

0