Abstract
Bacterial scab and viral diseases of plants caused by the whitefly represent a prolem that attracts the attention of biologists. Among the many species of whiteflies, the B.tabaci, is an insect able to attack multiple crops, weeds, and ornamental hosts. Their small size belies, their ability to reproduce quickly and their skills for moving over relatively short distances contribute to put several potential hosts at the risk of infestation. Plant protection against these insects is critical for increasing crop quantity and quality. At the time and up to now, farmers have used conventional and manual means of protection against this insect. So, an effective protection strategy must start with early detection and identification of this type of insect in order to know if it is a female egg. In recent years, deep learning in general and auto-encoders have given excellent results in many images classification tasks. This gave us the opportunity to improve the accuracy of classification in the field of agriculture and the identification of insect eggs on plants.
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References
Hanssen, M., Lapidot, M.: Major tomato viruses in the mediterranean basin. Adv. Virus Res. 84 (2012)
Blancard, D.: Tomato diseases. Academic Press the Netherlands (2012)
Viral diseases. Disponible depuis: 8https ://ausveg.com.au/biosecurity-agrichemical/crop protection/overview-pests-diseases-disorders/viral diseases/. Consulté le 27 mai aux environs de 15h30
Jemai, O., Zaied, M., Ben Amar, C.: Fast learning algorithm of wavelet network based on fast wavelet transform. Int. J. Pattern Recogn. Artifi. Intell. 25(8) 1297–319 (2011)
Zaied, M., Said, S., Jemai, O., Ben Amar, C.: A novel approach for face recognition based on fast learning algorithm and wavelet network theory. Int. J. Wavelets Multiresolution Inf. Process. 9(6) 923–945 (2011)
ElAdel, A., Ejbali, R., Zaied, M., Amar, C.B.: A new system for image retrieval using beta wavelet network for descriptors extraction and fuzzy decision support. SoCPaR 232–236 (2014)
Nhidi, W., Ejbali, R., Dahman, H.: An intelligent approach to identify parasitic eggs from a slender-billed’s nest. In: Proceedings SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019) (2019)
ElAdel, A., Ejbali, R., Zaied, M., Amar, C.B.: Deep learning with shallow architecture for image classification. In: International Conference on High Performance Computing & Simulation (HPCS) (2015)
Hassairi, S., Ejbali, R., Zaied, M.: A deep convolutional neural wavelet network to supervised Arabic letter image classification. In: 15th International Conference on Intelligent Systems Design and Applications (2015)
Wiem, N., Ali, C.M., Ridha, E.: Wavelet Feature with CNN for Identifying Parasitic Egg from a Slender-Billed’s Nest In: International Conference on Hybrid Intelligent SystemsHIS (2020)
ElAdel, A., Ejbali, R., Zaied, M.: A hybrid approach for content-based image retrieval based on fast beta wavelet network and fuzzy decision support system. CB Amar Mach. Vision Appli. 27(6), 781–799
Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Mahmoudi, S., Nhidi, W., Bennour, C., Ben Belgacem, A., Ejbali, R. (2023). An Intelligent Approach to Identify the Eggs of the Insect Bemisia Tabaci. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_7
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