Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application
In this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/4/2/34 |
| _version_ | 1797490920231272448 |
|---|---|
| author | Chinmay U. Parab Canicius Mwitta Miller Hayes Jason M. Schmidt David Riley Kadeghe Fue Suchendra Bhandarkar Glen C. Rains |
| author_facet | Chinmay U. Parab Canicius Mwitta Miller Hayes Jason M. Schmidt David Riley Kadeghe Fue Suchendra Bhandarkar Glen C. Rains |
| author_sort | Chinmay U. Parab |
| collection | DOAJ |
| description | In this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap were recorded 2× per day. A total of 120 whitefly images were labeled using labeling software and split into a training and testing dataset, and 18 additional yellow-stick tape images were labeled with false positives to increase the model accuracy from remote whitefly monitors in the field that created false positives due to water beads and reflective light on the tape after rain. The two-shot detection model has two stages: region proposal and then classification of those regions and refinement of the location prediction. Single-shot detection skips the region proposal stage and yields final localization and content prediction at once. Because of this difference, YOLOv4 is faster but less accurate than Faster-RCNN. From the results of our study, it is clear that Faster-RCNN (precision—95.08%, F-1 Score—0.96, recall—98.69%) achieved a higher level of performance than YOLOv4 (precision—71.77%, F-1 score—0.83, recall—73.31%), and will be adopted for further development of the monitoring station. |
| first_indexed | 2024-03-10T00:39:57Z |
| format | Article |
| id | doaj.art-1808611f493743dfbccfd531f313d2b6 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T00:39:57Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-1808611f493743dfbccfd531f313d2b62023-11-23T15:08:35ZengMDPI AGAgriEngineering2624-74022022-06-014250752210.3390/agriengineering4020034Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web ApplicationChinmay U. Parab0Canicius Mwitta1Miller Hayes2Jason M. Schmidt3David Riley4Kadeghe Fue5Suchendra Bhandarkar6Glen C. Rains7Department of Computer Science, University of Georgia, Athens, GA 30602, USADepartment of Entomology, University of Georgia, Tifton, GA 31793, USADepartment of Entomology, University of Georgia, Tifton, GA 31793, USADepartment of Entomology, University of Georgia, Tifton, GA 31793, USADepartment of Entomology, University of Georgia, Tifton, GA 31793, USADepartment of Agricultural Engineering (DAE), Sokoine University of Agriculture, Morogoro 30007, TanzaniaDepartment of Computer Science, University of Georgia, Athens, GA 30602, USADepartment of Entomology, University of Georgia, Tifton, GA 31793, USAIn this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap were recorded 2× per day. A total of 120 whitefly images were labeled using labeling software and split into a training and testing dataset, and 18 additional yellow-stick tape images were labeled with false positives to increase the model accuracy from remote whitefly monitors in the field that created false positives due to water beads and reflective light on the tape after rain. The two-shot detection model has two stages: region proposal and then classification of those regions and refinement of the location prediction. Single-shot detection skips the region proposal stage and yields final localization and content prediction at once. Because of this difference, YOLOv4 is faster but less accurate than Faster-RCNN. From the results of our study, it is clear that Faster-RCNN (precision—95.08%, F-1 Score—0.96, recall—98.69%) achieved a higher level of performance than YOLOv4 (precision—71.77%, F-1 score—0.83, recall—73.31%), and will be adopted for further development of the monitoring station.https://www.mdpi.com/2624-7402/4/2/34object detectionconvolutional neural networkwhitefliesweb appinsect trapIoT |
| spellingShingle | Chinmay U. Parab Canicius Mwitta Miller Hayes Jason M. Schmidt David Riley Kadeghe Fue Suchendra Bhandarkar Glen C. Rains Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application AgriEngineering object detection convolutional neural network whiteflies web app insect trap IoT |
| title | Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application |
| title_full | Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application |
| title_fullStr | Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application |
| title_full_unstemmed | Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application |
| title_short | Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application |
| title_sort | comparison of single shot and two shot deep neural network models for whitefly detection in iot web application |
| topic | object detection convolutional neural network whiteflies web app insect trap IoT |
| url | https://www.mdpi.com/2624-7402/4/2/34 |
| work_keys_str_mv | AT chinmayuparab comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT caniciusmwitta comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT millerhayes comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT jasonmschmidt comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT davidriley comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT kadeghefue comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT suchendrabhandarkar comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication AT glencrains comparisonofsingleshotandtwoshotdeepneuralnetworkmodelsforwhiteflydetectioniniotwebapplication |