https://doi.org/10.4081/jae.2022.1381
An intelligent system for detecting Mediterranean fruit fly [Medfly; Ceratitis capitata (Wiedemann)]
Published: 30 June 2022
Abstract Views: 1585
PDF: 498
HTML: 254
HTML: 254
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly’s existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap’s sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.
Akbas A., Yildiz H., Ozbayoglu M., Tavli B. 2019. Neural network based instant parameter prediction for wireless sensor network optimization models. Wireless Netw. 25:3405-18.
Amazon Web Services. 2021. What is data labeling for Machine Learning? Available from: https://aws.amazon.com/tr/sagemaker/groundtruth/what-is-data-labeling/ Accessed: 22 June, 2021.
Apolo O.E., Martinez G.J., Egea G., Raja P., Pérez-R.M. 2020. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur. J. Agron. 115:126030.
Bekker G., Addison P., Van Niekerk A. 2019. Using machine learning to identify the geographical drivers of Ceratitis capitata trap catch in an agricultural landscape. Comput. Electron. Agric. 162:582-92.
Cohen Y., Cohen A., Hetzroni A., Alchanatis V., Broday D., Gazit Y., Timar D. 2007. Spatial decision support system for Medfly control in citrus. Elsevier B.V. Comput. Electron. Agric. 62:107-17.
Ding W., Taylor G. 2016. Automatic moth detection from trap images for pest management. Comput. Electron. Agric. 123:17-28.
Eliza W. 2017. Call to cull falled fruit as Carnarvon mango season fire sup fruit fly pests. ABC News. Available from: https://www.abc.net.au/news/rural/2017-01-18/carnarvon-mango-season-fires-up-the-fruit-flies/8191276 Accessed: 26 March, 2020.
Espinoza K., Valera D.L., Torres J.A., López A., Molina-Aiz F.D. 2016. Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Comput. Electron. Agric. 127:495-505.
Estres vegetal. 2020. Cera trap. Available from: https://www.youtube.com/watch?v=s8HOGAF8vi4 Accessed: 17 December, 2020.
Fruitfly Africa. 2021. Life cycle. Available from: http://www.fruitfly.co.za/life-cycle-and-identification/life-cycle/ Accessed: 20 June, 2021.
Gad A.F. 2020. Faster R-CNN Explained for object detection tasks. Available from: https://blog.paperspace.com/faster-r-cnn-explained-object-detection/ Accessed: 04 April, 2021.
Galdames J.P.M., Milhor C.E., Becker M. 2018. Citrus fruit detection using Faster R-CNN algorithm under real outdoor conditions. In Proceedings of the 14th International Conference on Precision Agriculture (unpaginated, online), Monticello, IL, USA.
International Society of Precision Agriculture, June 24 – June 2, 2018, Montreal, Quebec, Canada.
Github Inc. 2021. LabelImg. Available from: https://github.com/tzutalin/labelImg Accessed: 22 June, 2021.
Goldshtein E., Cohen Y., Hetzroni A., Gazit Y., Timar D., Rosenfeld L., Grinshpon Y., Hoffman A., Mizrach A. 2017. Development of an automatic monitoring trap for Mediterranean fruit fly (Ceratitis capitata) to optimize control applications frequency. Comput. Electron. Agric. 139:115-25.
Hong S.-J., Kim S.-Y., Kim E., Lee C., Lee J.-S., Lee D.-S., Bang J., Kim G. 2020. Moth detection from pheromone trap images using deep learning object detectors. Agriculture 10:170.
Jonathan H. 2018. mAP (mean Average Precision) for object detection. Available from: https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173 Accessed: 18 November, 2021.
JSacadura, 2020. An effective fruit fly trap for the home orchard, reducing medfly damage in apples and other fruits. Available from: https://www.youtube.com/watch?v=aDBDdLxw0Hw> Accessed: 17 December, 2020.
Kasinathan T., Singaraju D., Reddy U.S. 2020. Insect classification and detection in field crops using modern machine learning techniques. Inf. Process. Agric. 8. [Epub ahead of print].
Li W., Wang D., Li M., Gao Y., Wu J., Yang X. 2021. Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Comput. Electron. Agric. 183.
Ministry of Agriculture and Forestry. 2019. Enstitüler. Available from: https://www.tarimorman.gov.tr/TAGEM/Link/13/Enstituler. Accessed: 23 June, 2019.
Moraes F., Nava D., Scheunemann T., Rosa V. 2019. Development of an optoelectronic sensor for detecting and classifying fruit fly (Diptera: Tephritidae) for use in real-time intelligent traps. Sensors. 19:1254.
Padilla R., Netto S.L., Da Silva E.A.B. 2020. A survey on performance metrics for object-detection algorithms. pp. 237-242 in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP).
Patrício D.I., Rieder R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153:69-81.
Picbear. 2020. Trapped Medflies. Available from: https://www.picbear.org/media/ Accessed: 26 March, 2020.
Remboski T.B., Souza W., Aguiar M., Ferreira J.R. 2018. Identification of fruit fly in intelligent traps using techniques of digital image processing and machine learning. pp. 260-267 in SAC ‘18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing.
Ren S., He K., Girshick R., Sun J. 2016. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Machine Intell. 39:1137-49.
Rustia Dan J., Lin Chien E., Chung J.-Y., Zhuang Y., Hsu J.-C., Lin T.-T. 2020. Application of an image and environmental sensor network for automated greenhouse insect pest monitoring. J. Asia-Pacific Entomol. 23:17-28.
SEDQ Healthy Crops S.L. 2020. Ceratipack: use in mass trapping for the management of the Mediterranean fruit fly Ceratitis capitata. Available from: https://sedq.es/en/producto/ceratipack/ Accessed: 11 April, 2020.
Sorhocam. 2021a. Akdeniz Meyve Sineği (Ceratitis Capitata). Available from: https://sorhocam.com/uploads/docs/akdeniz-meyve-sinegi-04547.pdf Accessed: 20 June, 2021.
Sorhocam. 2021b. Akdeniz Meyve Sineği (Ceratitis Capitata). Available from: https://sorhocam.com/konu.asp?sid=142&akdeniz-meyve-sinegi-ceratitis-capitata.html Accessed: 10 June, 2021.
Stackoverflow. 2016. Why normalize images by subtracting dataset’s image mean, instead of the current image mean in deep learning? Available from: https://stats.stackexchange.com/questions/211436/why-normalize-images-by-subtracting-datasets-image-mean-instead-of-the-current Accessed: 16 November, 2021.
Stackoverflow. 2017. How does mean image subtraction work? Available from: https://stackoverflow.com/questions/44788133/how-does-mean-image-subtraction-work Accessed: 16 November, 2021.
Sun Y., Liu X., Yuan M., Ren L., Wang J., Chen Z., 2018. Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring. Biosyst. Engine. 176:140-50.
The University of Arizona. 2021. The Mediterranean Fruit Fly Ceratitis Capitata (Wiedemann), Available from: https://cals.arizona.edu/crops/insects/fruitfly.pdf Accessed: March 07, 2021.
Umruh A. 2017. What is the Tensorflow machine intelligence platform? Available from: https://opensource.com/article/17/11/intro-tensorflow Accessed: 26 June, 2019.
University of Georgia Center for Invasive Species and Ecosystem Health. 2020. Invasive species. Available from: https://www.bugwood.org/ Accessed: 4 May, 2020.
USDA NASS (United States Department of Agriculture, National Agricultural Statistics Service). 2012. Mediterranean fruit fly pest profile, host, and economic importance. Available from: https://www.cdfa.ca.gov/plant/pdep/target_pest_disease_profiles/mediterranean_ff_profile.html Accessed: 18 June, 2019.
Wenyong L., Tengfei Z., Zhankui Y., Ming L., Chuanheng S., Xinting Y. 2021. Classification and detection of insects from field images using deep learning for smart pest management: a systematic review. Ecol. Inf. 66:101460.
Xie C., Wang R., Zhang J., Chen P., Dong W., Li R., Chen T., Chen H. 2018. Multi-level learning features for automatiic classification of field crop pests. Comput. Electron. Agric. 152:233-41.
ZF. 2010. Mediterranean fruit fly (Medfly). Available from: https://www.youtube.com/watch?v=56x7n1IS8Ns Accessed: 17 December, 2020.
How to Cite
Uzun, Y. (2022) “An intelligent system for detecting Mediterranean fruit fly [Medfly; <em>Ceratitis capitata</em> (Wiedemann)]”, Journal of Agricultural Engineering, 53(3). doi: 10.4081/jae.2022.1381.
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Maria Lisa Clodoveo, Tiziana Dipalmo, Cristina Schiano, Domenico La Notte, Sandra Pati, What’s now, what’s new and what’s next in virgin olive oil elaboration systems? A perspective on current knowledge and future trends , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Roberto Romaniello, Giorgio Peri, Alessandro Leone, Fluorescence hyper-spectral imaging to detecting faecal contamination on fresh tomatoes , Journal of Agricultural Engineering: Vol. 47 No. 1 (2016)
- Lorenzo Vergni, Alessandra Vinci, Francesca Todisco, Francesco Saverio Santaga, Marco Vizzari, Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Luisa Martelloni, Christian Frasconi, Mino Sportelli, Marco Fontanelli, Michele Raffaelli, Andrea Peruzzi, Hot foam and hot water for weed control: A comparison , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Maria Luisa Amodio, Antonio Derossi, Giancarlo Colelli, Modelling sensorial and nutritional changes to better define quality and shelf life of fresh-cut melons , Journal of Agricultural Engineering: Vol. 44 No. 1 (2013)
- Carmelo Riccardo Fichera, Luigi Laudari, Giuseppe Modica, Application, validation and comparison in different geographical contexts of an integrated model for the design of ecological networks , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Fabiana Convertino, Ileana Blanco, Evelia Schettini, Giuliano Vox, A nature-based system for improving Mediterranean buildings’ performance: contribution to energy saving by heat transfer reduction and influence of climatic parameters , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Danilo Monarca, Massimo Cecchini, Abstracts of the 10th Conference of the Italian Society of Agricultural Engineering , Journal of Agricultural Engineering: Vol. 44 No. s1 (2013): Abstract Book
- Remo Alessio Malagnino, Performance analysis of photovoltaic plants installed in dairy cattle farms , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Alvaro Marucci, Danilo Monarca, Andrea Colantoni, Enio Campiglia, Andrea Cappuccini, Analysis of the internal shading in a photovoltaic greenhouse tunnel , Journal of Agricultural Engineering: Vol. 48 No. 3 (2017)
<< < 7 8 9 10 11 12 13 14 15 16 > >>
You may also start an advanced similarity search for this article.
