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

作　　者：Anas Berka Adel Hafiane Youssef Es-Saady Mohamed El Hajji Raphaël Canals Rachid Bouharroud 

机构地区：[1]IRF-SIC Laboratory,Ibnou Zohr University,BP 8106—Cite Dakhla,Agadir 80000,Morocco [2]INSA CVL,University of Orleans,PRISME Laboratory,EA 4229,Bourges 18022,France [3]Regional Center for Agricultural Research of Agadir,National Institute of Agricultural Research,Avenue Ennasr,PoB 415 Rabat Principale,Rabat 10090,Morocco

出　　处：《Artificial Intelligence in Agriculture》2023年第3期12-21,共10页农业人工智能（英文）

基　　金：PHC Toubkal/21/121-Campus France:45942UG who funded the work and the mobility fee between Morocco and France.

摘　　要：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 products.However,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 stage.Current solutions consist of regular field checks by the naked eyes carried out by experts.The major difficulty is the lack of experts to check all fields,especially in remote areas.In addition,this requires time and resources.Hence the need for a system that can categorize the health level of cacti remotely.To 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 disease.Since 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 crops.In this work,we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer(ViT)model.We 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 class.This study compares the existing models on the new dataset and presents the results obtained.The 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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]