An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

Gavhale, Ms. Kiran R.; Gawande, Prof. Ujwalla

doi:10.9790/0661-16151016

Cited by 177 publications

(38 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the popularity of machine learning algorithms in computer vision, in order to improve the accuracy and rapidity of the diagnosis results, researchers have studied automated plant disease diagnosis based on traditional machine learning algorithms, such as random forest, k-nearest neighbor, and Support Vector Machine (SVM) [3][4][5][6][7][8][9][10][11][12]. However, because the classification features are selected and adopted based on human experience, these approaches improved the recognition accuracy, but the recognition rate is still not high enough and is vulnerable to artificial feature selection.…”

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

View full text Add to dashboard Cite

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

View full text Add to dashboard Cite

show abstract

“…For to 11. Calculate the velocity of particle using (1) 12. Update the position of particle using 213.…”

Section: Inputmentioning

confidence: 99%

“…The micro-organism pathogens can easily spread from one leaf to another that causes citrus canker [1]. The cause of the leprosis [2] has always been considered to be a virus transmitted by mite species.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Based Detection of Citrus Plant Diseases and Their Severity Level

K*,

2020

IJITEE

View full text Add to dashboard Cite

The quality of food production is reduced directly through plant diseases. The citrus plants are widely grown fruits worldwide. Each year, a large amount of waste is produced by citrus manufacturers, who annually destroy 50 percent of the citrus peel due to various plant diseases. The discovery of citrus plant diseases and their severity level will improve the quality of agricultural production. Image processing techniques are widely for detection of citrus plant disease. In this paper, evolutionary algorithms are introduced to detect citrus plant diseases and their severity level. The leaf images of citrus plants are collected and those images are pre-processed by removing noise using filtering technique. Then, the diseased portion in the leaf image is extracted by partitioning the image into multiple segments. From the segmented images, the features such as contrast, color, energy, local homogeneity, cluster shade and prominence are extracted using co-occurrence method and these features are processed in GA and PSO to detect the citrus plant diseases and their severity level. In GA, each gene randomly selects the features and classification rule is formed by chromosome. In PSO, each particle randomly selects the features and classification rule is generated. The best classification rule is selected based on the classification accuracy of selected classification rule. After the selection of best classification rule, it is applied to the testing data to detect citrus plant diseases and their severity level.

show abstract

“…The literature for plants disease detection conveys that colour and texture play an important role in disease lesion classification [24]. Thus, several colour features, texture features, and their combinations are explored in this study to design a valuable system and to validate its performance.…”

Section: Feature Extractionmentioning

confidence: 99%

Semi‐automatic leaf disease detection and classification system for soybean culture

Sandeep

Pandey

Goel

2018

IET Image Processing

152

View full text Add to dashboard Cite

An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

Abstract: Diseases in plants cause major production and economic losses as well as reduction in both quality

Cited by 177 publications

References 10 publications

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Swarm Intelligence Based Detection of Citrus Plant Diseases and Their Severity Level

Semi‐automatic leaf disease detection and classification system for soybean culture

Contact Info

Product

Resources

About