.png){kind=icon}
返回
Coffee disease classification at the edge using deep learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.atech.2023.100183.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Brazil is the world's largest producer and exporter of coffee and the second largest consumer of the beverage. The aim of this study is to embed convolutional networks in a low-cost microcontrolled board to classify coffee leaf diseases in loco, without the need for an internet connection. Early identification of diseases in coffee plantations is crucial for productivity and production quality. Two datasets were used, in addition to images taken with the development board itself, totaling more than 6000 images of six different types of diseases. The proposed architectures (cascade and single-stage), when embedded, presented accuracy values around 98% and 96%, respectively, demonstrating their ability to assist in the diagnosis of diseases in coffee farms, especially those managed by producers with less resources.
Keyword:
Artificial intelligence
Convolutional networks
Edge computing
Deep learning
Coffee diseases
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
2.6K
被引数:
2.5K
机构
引用论文
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition基于深度学习的番茄病虫害实时识别检测器.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Promoting Patriarchy or Dual Equality? Multiculturalism and the Immigrant Household Division of Labor倡导父权制还是双重平等？多元文化主义与移民家庭劳动分工OAAI
OAAI分享收藏
查看更多内容