AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment
The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production envi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/4/731 |
| _version_ | 1797538177503723520 |
|---|---|
| author | Pedro Faria Telmo Nogueira Ana Ferreira Cristina Carlos Luís Rosado |
| author_facet | Pedro Faria Telmo Nogueira Ana Ferreira Cristina Carlos Luís Rosado |
| author_sort | Pedro Faria |
| collection | DOAJ |
| description | The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation. |
| first_indexed | 2024-03-10T12:26:51Z |
| format | Article |
| id | doaj.art-3ebc3edbf5e24ba4a05d28752260f0f8 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T12:26:51Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-3ebc3edbf5e24ba4a05d28752260f0f82023-11-21T14:56:54ZengMDPI AGAgronomy2073-43952021-04-0111473110.3390/agronomy11040731AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy AssessmentPedro Faria0Telmo Nogueira1Ana Ferreira2Cristina Carlos3Luís Rosado4Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 44200-135 Porto, PortugalGeoDouro—Consultoria e Topografia, Lda., Av. D. Egas Moniz, BL 3 R/C Dt°, Quinta dos Prados, Rina, 5100-196 Lamego, PortugalAssociation for the Development of Viticulture in the Douro Region (ADVID), Science and Technology Park of Vila Real—Régia Douro Park. 5000-033 Vila Real, PortugalAssociation for the Development of Viticulture in the Douro Region (ADVID), Science and Technology Park of Vila Real—Régia Douro Park. 5000-033 Vila Real, PortugalFraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 44200-135 Porto, PortugalThe increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation.https://www.mdpi.com/2073-4395/11/4/731viticulturepests monitoringinsect trapsmobile image acquisitionimage quality assessmentimage adequacy assessment |
| spellingShingle | Pedro Faria Telmo Nogueira Ana Ferreira Cristina Carlos Luís Rosado AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment Agronomy viticulture pests monitoring insect traps mobile image acquisition image quality assessment image adequacy assessment |
| title | AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment |
| title_full | AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment |
| title_fullStr | AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment |
| title_full_unstemmed | AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment |
| title_short | AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment |
| title_sort | ai powered mobile image acquisition of vineyard insect traps with automatic quality and adequacy assessment |
| topic | viticulture pests monitoring insect traps mobile image acquisition image quality assessment image adequacy assessment |
| url | https://www.mdpi.com/2073-4395/11/4/731 |
| work_keys_str_mv | AT pedrofaria aipoweredmobileimageacquisitionofvineyardinsecttrapswithautomaticqualityandadequacyassessment AT telmonogueira aipoweredmobileimageacquisitionofvineyardinsecttrapswithautomaticqualityandadequacyassessment AT anaferreira aipoweredmobileimageacquisitionofvineyardinsecttrapswithautomaticqualityandadequacyassessment AT cristinacarlos aipoweredmobileimageacquisitionofvineyardinsecttrapswithautomaticqualityandadequacyassessment AT luisrosado aipoweredmobileimageacquisitionofvineyardinsecttrapswithautomaticqualityandadequacyassessment |