scholarly journals Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images

2021 ◽  
Vol 1845 (1) ◽  
pp. 012009
Author(s):  
Arnes Sembiring ◽  
Yuwaldi Away ◽  
Fitri Arnia ◽  
Rusdha Muharar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tomato Plant ◽  
Plant Disease ◽  
Disease Classification

scholarly journals Plant Disease Classification using Lite Pretrained Deep Convolutional Neural Network on Android Mobile Device

2019 ◽  
Vol 9 (2) ◽  
pp. 2796-2804
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Mobile Device ◽  
Plant Disease ◽  
Detection System ◽  
Disease Classification ◽  
Latency Time ◽  
Time Latency ◽  
Persistence Model

The implementation of image recognition in agriculture to detect symptoms of plant disease using deep learning Convolutional Neural Network (CNN) models are proven to be highly effective. The computational efficiency by using CNN, made possible to run the application on mobile device. To optimize the utilization of mobile device and choosing the most effective CNN model to run as detection system in mobile device with the highest accuracy and low resource consumption is proposed in this paper. In this study, PlantVillage dataset which extended to coffee leaf, were tested and compared using three CNN models, two models which specifically designed for mobile, MobileNet and Mobile Nasnet (MNasNet), and one model that recognized for its accuracy on personal computer (PC), InceptionV3. The experiment executed on both mobile and PC found a slightly degradation on accuracy when the application is running on mobile. InceptionV3 experienced the most persistence model compares to MNasNet and MobileNet. Yet, InceptionV3 had biggest latency time. The final result on mobile device recorded InceptionV3 achieved highest accuracy of 95.79%, MNasNet 94.87%, and MobileNet 92.83%, while for time latency MobileNet achieved the lowest with 394.70 ms, MNasnet 430.20 ms, and InceptionV3 2236.10 ms respectively. It is expected that the outcome of this study will be of great benefit to farmers as mobile image recognition would help them analyze the condition of their plants on site simply by taking a picture of the leaf and running the experiment on their mobile device

scholarly journals Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 707
Author(s):  
Jinzhu Lu ◽  
Lijuan Tan ◽  
Huanyu Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Classification ◽  
Classification Methods ◽  
Widespread Application ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Traditional Classification

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

scholarly journals Light Intensity Adjustment and Severity Estimation for the Generic Disease of Tomato Plants

2021 ◽  
pp. 353-358
Author(s):  
Faizan Ahmed Sayyad ◽  
Rehan Ahmed Sayyad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crop Production ◽  
Disease Classification ◽  
Attention Networks ◽  
Severity Estimation ◽  
Image Capturing ◽  
Severity Of The Disease ◽  
Key Steps

Many research has been done on detecting the type of disease that affects the crops. Because of this the farmers use pesticides to reduce the loss of crop production, since they don’t know how much pesticides to spray or use, they tend to overuse them which eventually leads to further destruction of crops. For disease classification Convolutional Neural Network (CNN) is being used which lets you know what kind of disease has affected the crop. In this paper we have worked on self attention networks to calculate the severity of the disease on the leaf . Self Attention Network introduced in the architecture lets the model learn the feature more efficiently and focus more on the affected region of the leaf. The model was trained and tested on the standard dataset (Plant Village) . The core processes comprises image capturing, image processing and testing on Self Attention Convolutional Neural Network architecture.. All of the key steps required to implement the model are detailed throughout the document.

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