Detection and Analysis of Plant Leaf Diseases Using Convolutional Neural Network

2020 ◽  
Vol 17 (9) ◽  
pp. 3899-3903
Author(s):  
K. R. Mamatha ◽  
Seema Singh ◽  
S. A. Hariprasad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Plant Diseases ◽  
Convolution Neural Network ◽  
Plant Leaf ◽  
Indian Economy ◽  
Plant Products ◽  
Food Producer ◽  
The World ◽  
World Agriculture

India is the second largest food producer in the world. Agriculture plays an important role in Indian economy. Plants that are grown may experience many diseases which if not detected might lead to reduction of crops and further leads to the reduction of quality and quantity of plant products to be delivered to the market. Diseases can occur and the farmer may not be in a condition to identify the disease and he might be unaware of which pesticide to be used and the precautions to be taken in order to over-come it in future. This paper describes as how to detect common diseases a plant might get and the remedies that can be used to overcome them. Convolution neural network is used to classify the plant diseases and suggesting the remedies.

scholarly journals Plant Leaf Diseases Prediction and Classification Using Optimization Based Convolution Neural Network

2020 ◽  
pp. 174-176
Author(s):  
Mohan M ◽  
Vijayaganth V ◽  
Naveenkumar M
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Network Approach ◽  
Plant Leaves ◽  
Neural Network Approach ◽  
Plant Leaf ◽  
Network Support ◽  
Highlight Extraction

Plant leaf diseases and ruinous bugs are a significant test in the horticulture area. Quicker and an exact forecast of leaf diseases in plant could assist with building up an early treatment strategy while extensively decreasing financial misfortunes. Current progressed advancements in profound learning permitted analysts to amazingly improve the presentation and exactness of article identification and acknowledgment frameworks. A profound learning-based way to deal with recognize leaf illnesses in various plants utilizing pictures of plant leaves. The picture handling ventures for plant illness recognizable proof incorporate obtaining of pictures, pre-preparing, division and highlight extraction. Focus in predominantly on the most used order systems in illness location of plants, for example, Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been seen from the examination that advancement Convolutional Neural Network approach gives better precision contrasted with the conventional methodologies. Optimization based CNN convolution neural network the proposed framework can viably recognized various sorts of diseases with the capacity to manage complex situations from a plant's region.

scholarly journals Classification of Plant Leaf Diseases using CNN

2020 ◽  
Vol 9 (4) ◽  
pp. 1750-1754
Keyword(s):  
Neural Network ◽  
Plant Diseases ◽  
Convolution Neural Network ◽  
Advanced Technology ◽  
Agricultural Field ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Apple Plant ◽  
Neural Network Algorithm

Agricultural productive is the dominant issue, which affects the economy of the country excessively. So detection of diseases in plants plays a major role in Agricultural field. In previous day’s farmers in the fields used to observe the plants just by seeing with their eye for identification of a disease. But this method may take lot of time, expensive and inaccurate. So advanced technology that can identify plant diseases as easily as possible is needed, in order to decrease the percentage rate of the contamination of crops and increase the fertility. Here in this paper techniques like preprocessing, segmentation and classification of image are used. Here Tomato, Maize, Grape, Potato and Apple plant leaves are used, where different diseases are identified for each plant. For Classification we used Convolution Neural Network Algorithm, so that we can automatically detect the plant leaf diseases. And this will help farmers to identify their diseases as early as possible.

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 A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

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 Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Ma ◽  
Xueliang Guo ◽  
Shuke Zhao ◽  
Doudou Yin ◽  
Yiyi Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Abiotic Factors ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Identification Methods ◽  
Yield And Quality ◽  
Disease Identification ◽  
Deep Convolution Neural Network ◽  
And Control

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.

scholarly journals Electrocardiogram Classification for Arrhythmia using Convolutional Neural Network 2D and Adabound Optimizer

2020 ◽  
Vol 8 (5) ◽  
pp. 1277-1284
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Transform Method ◽  
The World ◽  
Optimal Accuracy ◽  
Deadly Disease ◽  
Electrocardiogram Ecg

Cardiovascular disease is the number one deadly disease in the world. Arrhythmia is one of the types of cardiovascular disease which is hard to detect but by using the routine electrocardiogram (ECG) recording. Due to the variety and the noise of ECG, it is very time consuming to detect it only by experts using bare eyes.Learning from the previous research in order to help the experts, this research develop 11 layers Convolutional Neural Network 2D (CNN 2D) using MITBIH Arrhythmia Dataset. The dataset is firstly preprocessed by using wavelet transform method, then being segmented by R-peak method. The challenge is how to conquer the imbalance and small amount of data but still get the optimal accuracy. This research can be helpful in helping the doctors figure out the type of arrhythmia of the patient. Therefore, this research did the comparison of various optimizers attach in CNN 2D namely, Adabound, Adadelta, Adagrad, Amsbound, Adam and Stochastic Gradient Descent (SGD). The result is Adabound get the highest performance with 91% accuracy and faster 1s training duration than Adam which is approximately 18s per epoch.

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