IoT & AI in Smart Farming: Implications and Challenges
2022 7th International Conference on Communication and Electronics Systems (ICCES)（2022）
Dept. of Computer Applications
Abstract
Agriculture is a vital part in the life of people to survive in this world. An abundant food availability is highly essential for all living beings on this planet. Smart farming has been broadly considered as one of the furthermost feasible solutions for food inadequacy. In Modern era technologies has revolutionized the concept of agriculture into a new paradigm for better land productivity, better resource usage and better decision-making. The integration of the various trending technologies not only makes agriculture smarter and more efficient, but it also makes the entire system more sustainable. This paper reveals state-of-the-art technologies, their implications and the challenges associated with their implementation in the agriculture sector.
MoreTranslated text
Key words
Agriculture,Smart Farming,Internet of Things,Artificial Intelligence,Sensors.
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined