Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.