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Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm
- Source :
- Electronics, Vol 10, Iss 1754, p 1754 (2021), Electronics; Volume 10; Issue 15; Pages: 1754
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.
- Subjects :
- TK7800-8360
Computer Networks and Communications
Machine vision
Computer science
Image processing
02 engineering and technology
ComputingMethodologies_ARTIFICIALINTELLIGENCE
Trap (computing)
embedded system
insect pest counting
Data acquisition
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
biology
business.industry
Deep learning
deep learning
04 agricultural and veterinary sciences
biology.organism_classification
Microcontroller
Identification (information)
sticky paper trap
Hardware and Architecture
Control and Systems Engineering
Embedded system
Signal Processing
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
020201 artificial intelligence & image processing
Artificial intelligence
Electronics
business
Insect trap
Subjects
Details
- ISSN :
- 20799292
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....0f0dcf8b05c2be0f81dfa00962992689