2023
Lu, Guoyu; Li, Sheng; Mai, Gengchen; Sun, Jin; Zhu, Dajiang; Chai, Lilong; Sun, Haijian; Wang, Xianqiao; Dai, Haixing; Liu, Ninghao; Xu, Rui; Petti, Daniel; Li, Changying; Liu, Tianming; Li, Changying
AGI for Agriculture Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: 3D reconstruction, AGI, Deep convolutional neural network, deep learning, High-throughput phenotyping, object detection, phenotyping robot, robotics
@article{lu2023agi,
title = {AGI for Agriculture},
author = {Guoyu Lu and Sheng Li and Gengchen Mai and Jin Sun and Dajiang Zhu and Lilong Chai and Haijian Sun and Xianqiao Wang and Haixing Dai and Ninghao Liu and Rui Xu and Daniel Petti and Changying Li and Tianming Liu and Changying Li},
url = {https://arxiv.org/abs/2304.06136},
year = {2023},
date = {2023-04-12},
urldate = {2023-01-01},
abstract = {Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry. },
keywords = {3D reconstruction, AGI, Deep convolutional neural network, deep learning, High-throughput phenotyping, object detection, phenotyping robot, robotics},
pubstate = {published},
tppubtype = {article}
}
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry.