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CactiViT:Image-based smartphone application and transformer network for diagnosis of cactus cochineal

Artificial Intelligence in Agriculture 2023年第3期9卷 12-21页

作者： anas berka Adel Hafiane Youssef Es-Saady Mohamed El Hajji Raphaël Canals Rachid Bouharroud IRF-SIC Laboratory Ibnou Zohr UniversityBP 8106—Cite DakhlaAgadir 80000Morocco INSA CVL University of OrleansPRISME LaboratoryEA 4229Bourges 18022France Regional Center for Agricultural Research of Agadir National Institute of Agricultural ResearchAvenue EnnasrPoB 415 Rabat PrincipaleRabat 10090Morocco

The cactus is a plant that grows in many rural areas,widely used as a hedge,and has multiple benefits through the manufacture of various cosmetics and other ***,this crop has been suffering for some time from the attack of the carmine scaleDactylopius opuntia(Hemiptera:Dactylopiidae).The infestation can spread rapidly if not treated in the early *** solutions consist of regular field checks by the naked eyes carried out by *** major difficulty is the lack of experts to check all fields,especially in remote *** addition,this requires time and *** the need for a system that can categorize the health level of cacti *** date,deep learning models used to categorize plant diseases from images have not addressed the mealy bug infestation of cacti because computer vision has not sufficiently addressed this *** there is no public dataset and smartphones are commonly used as tools to take pictures,it might then be conceivable for farmers to use them to categorize the infection level of their *** this work,we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer(ViT)*** also provided a new image dataset of cochineal infested cacti.1 Finally,we developed a mobile application that delivers the classification results directly to farmers about the infestation in their fields by showing the probabilities related to each *** study compares the existing models on the new dataset and presents the results *** VIT-B-16 model reveals an approved performance in the literature and in our experiments,in which it achieved 88.73%overall accuracy with an average of+2.61%compared to other convolutional neural network(CNN)models that we evaluated under similar conditions.

关键词： Cactus Cochineal Smartphones Classification VIT Deep learning

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