WeChat Mini Program
Old Version Features

An Improved Light Efficiency Led Array Design by Increasing Uniformity for Pea Sprouts

Li Du, Yang Xu, Can Wang, Yiyi Chen,Junhua Zhang, Bin Ma, Danyan Chen

openalex(2024)

Cited 0|Views3
Abstract
Light distribution is one of the important factors affecting plant growth in facility lighting. However, the traditional lighting has the problem of uneven illumination, which increases the difficulty of cultivation management and brings challenges to standardized plant production. Achieving the best light environment is of great significance to the high efficiency production. In order to effectively solve the problem of uneven distribution of light in the planting layer, this study proposes a high light efficiency LED array design by increasing uniformity based on the improved genetic algorithm. It has been verified by optical simulation, spectrometer measurement and the pea sprouts cultivation. The simulation results showed that the illumination uniformity of optimized LED array and traditional square LED array were 91.72% and 85.74% respectively. The uniformity of illuminance measured by spectrometer were 92.40% and 80.11% respectively. In addition, the effects of two LED arrays on the growth of pea sprouts were compared. The total light intensity of the optimized LED array was reduced by 20.12%, but the yield of pea sprouts increased by 8.58%. The light intensity needed to produce pea sprouts per unit mass was reduced by 26.46%, and the energy efficiency and economic efficiency were also improved. Therefore, the LED array designed based on improved genetic algorithm has high illumination uniformity and light use efficiency, and provides a new idea for pea sprout production and lighting optimization strategy.
More
Translated text
Key words
Plant Development,Particle Swarm Optimization,Light Regulation,Optimization
求助PDF
上传PDF
Bibtex
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