Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification
作者机构：LTI LaboratoryENSAChouaib Doukkali University of El JadidaEl JadidaMorocco LAMSAD LaboratoryENSAHassan First UniversityBerrechidMorocco
出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)
年 卷 期：2022年第6卷第1期
页 面：77-89页
核心收录：
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Convolutional neuronal networks(CNN) Classification Optimization Gradient descent Plant diseases Olive dataset diseases(ODD)
摘 要：Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural ***,the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production ***,the detection of plant diseases-either with a naked eye or using traditional methods-is largely a cumbersome process in terms of time,availability and results with a high-risk *** present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease *** study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco,that also includes healthy class to detect olive ***,one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance *** highest rate in trained models was 100%,while the highest rate in experiments without data augmentation was 92,59%.Another subject of this study is the influence of the optimization algorithms on neuronal network *** a result of the experiments carried out,the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector.
维普期刊数据库