Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT
Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-con...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/18/5280 |
| _version_ | 1797553576229208064 |
|---|---|
| author | Balakrishnan Ramalingam Rajesh Elara Mohan Sathian Pookkuttath Braulio Félix Gómez Charan Satya Chandra Sairam Borusu Tey Wee Teng Yokhesh Krishnasamy Tamilselvam |
| author_facet | Balakrishnan Ramalingam Rajesh Elara Mohan Sathian Pookkuttath Braulio Félix Gómez Charan Satya Chandra Sairam Borusu Tey Wee Teng Yokhesh Krishnasamy Tamilselvam |
| author_sort | Balakrishnan Ramalingam |
| collection | DOAJ |
| description | Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy. |
| first_indexed | 2024-03-10T16:18:28Z |
| format | Article |
| id | doaj.art-4abf1da8086d4ab1a685b4e1af60b8e5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:18:28Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4abf1da8086d4ab1a685b4e1af60b8e52023-11-20T13:50:31ZengMDPI AGSensors1424-82202020-09-012018528010.3390/s20185280Remote Insects Trap Monitoring System Using Deep Learning Framework and IoTBalakrishnan Ramalingam0Rajesh Elara Mohan1Sathian Pookkuttath2Braulio Félix Gómez3Charan Satya Chandra Sairam Borusu4Tey Wee Teng5Yokhesh Krishnasamy Tamilselvam6Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeDepartment of Electrical Engineering, University of Western Ontario, London, ON N6A 3K7, CanadaInsect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.https://www.mdpi.com/1424-8220/20/18/5280insects detectionCNNdeep learningobject detectionIoTremote insect monitoring |
| spellingShingle | Balakrishnan Ramalingam Rajesh Elara Mohan Sathian Pookkuttath Braulio Félix Gómez Charan Satya Chandra Sairam Borusu Tey Wee Teng Yokhesh Krishnasamy Tamilselvam Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT Sensors insects detection CNN deep learning object detection IoT remote insect monitoring |
| title | Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT |
| title_full | Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT |
| title_fullStr | Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT |
| title_full_unstemmed | Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT |
| title_short | Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT |
| title_sort | remote insects trap monitoring system using deep learning framework and iot |
| topic | insects detection CNN deep learning object detection IoT remote insect monitoring |
| url | https://www.mdpi.com/1424-8220/20/18/5280 |
| work_keys_str_mv | AT balakrishnanramalingam remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT rajeshelaramohan remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT sathianpookkuttath remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT brauliofelixgomez remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT charansatyachandrasairamborusu remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT teyweeteng remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot AT yokheshkrishnasamytamilselvam remoteinsectstrapmonitoringsystemusingdeeplearningframeworkandiot |