摘要
Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Diseases brought on by bacteria,viruses,and fungi are a significant factor in reduced crop yields.Numerous machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved accuracy.This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves.To effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and VGG-16.Our focus was on optimizing the hyper-parameters of these deep learningmodels with prior training.For the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.
