Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning
As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/12/11/1967 |
| _version_ | 1797466097960615936 |
|---|---|
| author | Tiago Domingues Tomás Brandão Ricardo Ribeiro João C. Ferreira |
| author_facet | Tiago Domingues Tomás Brandão Ricardo Ribeiro João C. Ferreira |
| author_sort | Tiago Domingues |
| collection | DOAJ |
| description | As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5 for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach was used to minimize insect detection duplicates in a non-complex way. This article also contributes to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect-related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for <i>mAP_0.5</i>, with a precision and recall of 88% and 91%, respectively, using YOLOv5x. |
| first_indexed | 2024-03-09T18:31:56Z |
| format | Article |
| id | doaj.art-1d5691fa56fc48f4a40f6ad11002d562 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T18:31:56Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-1d5691fa56fc48f4a40f6ad11002d5622023-11-24T07:25:54ZengMDPI AGAgriculture2077-04722022-11-011211196710.3390/agriculture12111967Insect Detection in Sticky Trap Images of Tomato Crops Using Machine LearningTiago Domingues0Tomás Brandão1Ricardo Ribeiro2João C. Ferreira3Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisbon, PortugalInstituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisbon, PortugalINOV-INESC Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, PortugalInstituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisbon, PortugalAs climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5 for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach was used to minimize insect detection duplicates in a non-complex way. This article also contributes to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect-related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for <i>mAP_0.5</i>, with a precision and recall of 88% and 91%, respectively, using YOLOv5x.https://www.mdpi.com/2077-0472/12/11/1967pestsinsectsdetectionidentificationprecision agriculturemachine learning |
| spellingShingle | Tiago Domingues Tomás Brandão Ricardo Ribeiro João C. Ferreira Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning Agriculture pests insects detection identification precision agriculture machine learning |
| title | Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning |
| title_full | Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning |
| title_fullStr | Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning |
| title_full_unstemmed | Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning |
| title_short | Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning |
| title_sort | insect detection in sticky trap images of tomato crops using machine learning |
| topic | pests insects detection identification precision agriculture machine learning |
| url | https://www.mdpi.com/2077-0472/12/11/1967 |
| work_keys_str_mv | AT tiagodomingues insectdetectioninstickytrapimagesoftomatocropsusingmachinelearning AT tomasbrandao insectdetectioninstickytrapimagesoftomatocropsusingmachinelearning AT ricardoribeiro insectdetectioninstickytrapimagesoftomatocropsusingmachinelearning AT joaocferreira insectdetectioninstickytrapimagesoftomatocropsusingmachinelearning |