Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

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Detection and Classification of Leaf Disease Using Deep Neural Network

10.4018/978-1-7998-8161-2.ch004 ◽

2022 ◽

pp. 51-77

Author(s):

Meeradevi ◽

Monica R. Mundada ◽

Shilpa M.

Keyword(s):

Neural Network ◽

Plant Disease ◽

Early Stage ◽

Crop Productivity ◽

Attention Mechanism ◽

Disease Classification ◽

Production Plant ◽

Leaf Disease ◽

Proposed Model

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

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Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

Scalable Computing Practice and Experience ◽

10.12694/scpe.v21i4.1780 ◽

2020 ◽

Vol 21 (4) ◽

pp. 625-635

Author(s):

Anandhakrishnan T ◽

Jaisakthi S.M Murugaiyan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Early Stage ◽

Computing Time ◽

Network Models ◽

Deep Convolutional Neural Network ◽

Neural Network Models ◽

Leaf Disease ◽

Proposed Model ◽

Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

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Plant Leaf Disease Detection and Classification using Optimized CNN Model

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f5572.039621 ◽

2021 ◽

Vol 9 (6) ◽

pp. 233-238

Author(s):

Prabavathi S ◽

Kanmani P

Keyword(s):

Plant Disease ◽

Early Stage ◽

Disease Detection ◽

Severe Damage ◽

Plant Leaf ◽

Leaf Disease ◽

Accuracy Of Prediction ◽

Automatic Systems

Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.

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Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

Applied Sciences ◽

10.3390/app10041245 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1245 ◽

Cited By ~ 2

Author(s):

Valeria Maeda-Gutiérrez ◽

Carlos E. Galván-Tejada ◽

Laura A. Zanella-Calzada ◽

José M. Celaya-Padilla ◽

Jorge I. Galván-Tejada ◽

...

Keyword(s):

Operating Characteristic ◽

Area Under The Curve ◽

Plant Diseases ◽

Fine Tuning ◽

Network Architectures ◽

Tomato Plants ◽

Tomato Diseases ◽

Sensitivity Specificity

Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽

10.3390/s21082648 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2648

Author(s):

Muhammad Aamir ◽

Tariq Ali ◽

Muhammad Irfan ◽

Ahmad Shaf ◽

Muhammad Zeeshan Azam ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Natural Disasters ◽

Deep Convolutional Neural Network ◽

Multispectral Images ◽

Learning Techniques ◽

Proposed Model ◽

Disaster Intensity ◽

And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽

10.3390/agriculture11070651 ◽

2021 ◽

Vol 11 (7) ◽

pp. 651

Author(s):

Shengyi Zhao ◽

Yun Peng ◽

Jizhan Liu ◽

Shuo Wu

Keyword(s):

Neural Network ◽

High Performance ◽

Model Comparison ◽

Research Direction ◽

Disease Diagnosis ◽

Tomato Leaf ◽

Identification Accuracy ◽

Main Research ◽

Proposed Model ◽

Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

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Dual Protectıon for Data usıng Steganographıc Technıques with Embedded Framework

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1037.0785s319 ◽

2019 ◽

Vol 8 (5S3) ◽

pp. 155-159

Keyword(s):

High Performance ◽

Data Communication ◽

Original Data ◽

Cover Image ◽

Original Text ◽

Dual Protection ◽

Proposed Model ◽

Encryption And Decryption ◽

Secured Data

As the world is getting digitalized, the rush for need of secured data communication is overtop. Provoked by the vulnerability of human visual system to understand the progressive changes in the scenes, a new steganography method is proposed. The paper represents a double protection methodology for secured transmission of data. The original data is hidden inside a cover image using LSB substitution algorithm. The image obtained is inserted inside a frame of the video producing a stego-video. Stego-video attained is less vulnerable to attacks. After decryption phase, the original text is obtained which is error-free and the output image obtained is similar as the cover image. The quality of stego-video is high and there is no need for additional bandwidth for transmission. The hardware implement is required in order to calculate the corresponding analytical results. The proposed algorithm is examined and realized for various encryption standards using Raspberry Pi3 embedded hardware. The results obtained focuses on the attributes of the proposed model. On comparing with other conventional algorithms, the proposed scheme exhibits high performance in both encryption and decryption process with increase in efficiency of secured data communication.

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Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

10.3233/apc210108 ◽

2021 ◽

Author(s):

Hepzibah Elizabeth David ◽

K. Ramalakshmi ◽

R. Venkatesan ◽

G. Hemalatha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Early Stage ◽

Computational Effort ◽

Tomato Leaf ◽

Disease Detection ◽

Tomato Production ◽

Classification Image ◽

Leaf Disease ◽

Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

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Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

Current Bioinformatics ◽

10.2174/1574893616666210212084839 ◽

2021 ◽

Vol 16 ◽

Author(s):

Di Gai ◽

Xuanjing Shen ◽

Haipeng Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Melting Curve ◽

Classification Model ◽

Proposed Model ◽

Unbalanced Dataset ◽

Manual Selection ◽

Low Efficiency ◽

Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

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