Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

Mostafa, Almetwally M.; Kumar, Swarn Avinash; Meraj, Talha; Rauf, Hafiz Tayyab; Alnuaim, Abeer Ali; Alkhayyal, Maram Abdullah

doi:10.3390/app12010239

Cited by 44 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model achieved accuracy of 97.14% but inclusion of two Iinception units made VGG-16 more computational expensive. A work showcased various recent pre-trained models for disease detection in guava plants [40]. In [41], authors exploited pre-trained ResNet-50 model through transfer learning approach to identify disease in tomato crop with 97% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Vishnoi

Kumar

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.

show abstract

Section: Related Workmentioning

confidence: 99%

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Vishnoi

Kumar

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Deep learning and deep features [44] are extensively applied in many other applications [45][46][47][48]. By looking into their robustness in different aspects of complex problem solutions, the proposed methodology also applied them.…”

Section: Machine Learning-based Classificationmentioning

confidence: 99%

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

show abstract

“…Ahmad Almadhor et al [ 25 ] trained advanced classifiers for image-level and disease-level classification using a high-resolution guava leaf and fruit dataset and obtained an overall classification accuracy of 99%. Almetwally M. Mostafa et al [ 26 ] proposed an AI-Driven framework for the recognition of guava plant diseases through machine learning. After pre-processing and enhancing the data, enhanced data were then augmented over the nine angles using the affine transformation method—augmented enhanced data used by five DL networks by altering their last layers.…”

Section: Related Workmentioning

confidence: 99%

Maize leaf disease identification based on WG-MARNet

Zhou

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image’s resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method’s average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.

show abstract

Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

Cited by 44 publications

References 32 publications

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

Maize leaf disease identification based on WG-MARNet

Contact Info

Product

Resources

About