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

@article{Gavhale2014AnOO,
  title={An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques},
  author={Kiran R. Gavhale and Ujwalla Gawande},
  journal={IOSR Journal of Computer Engineering},
  year={2014},
  volume={16},
  pages={10-16}
}
Diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. [] Key Method This technique will improves productivity of crops. This paper also compares the benefits and limitations of these potential methods. It includes several steps viz. image acquisition, image pre-processing, features extraction and neural network based classification.

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References

SHOWING 1-10 OF 18 REFERENCES
Applying image processing technique to detect plant diseases
TLDR
A methodology for detecting plant diseases early and accurately, using diverse image processing techniques and artificial neural network (ANN) is proposed.
Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features
TLDR
The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%.
Grape leaf disease detection from color imagery using hybrid intelligent system
TLDR
This work presents automatic plant disease diagnosis using multiple artificial intelligent techniques and shows desirable results which can be further developed for any agricultural product analysis/inspection system.
Image recognition of plant diseases based on backpropagation networks
To achieve automatic diagnosis of plant diseases and improve the image recognition accuracy of plant diseases, two kinds of grape diseases (grape downy mildew and grape powdery mildew) and two kinds
Agricultural plant Leaf Disease DetectionUsing Image Processing
TLDR
The developed processing scheme consists of four main steps, a color transformation structure for the input RGB image is created, this RGB is converted to HSI because RGB is for color generation and his for color descriptor, then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed.
Image recognition of plant diseases based on principal component analysis and neural networks
TLDR
The results showed that neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained.
A Research of Maize Disease Image Recognition of Corn Based on BP Networks
TLDR
The experimental results show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more, and the study provided the theoretical basis to cognition of maize leaf disease.
Fast and Accurate Detection and Classification of Plant Diseases
TLDR
The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases and can achieve 20% speedup over the approach proposed in [1].
An Empirical Investigation of Olive Leave Spot Disease Using Auto-Cropping Segmentation and Fuzzy C-Means Classification
The objective of this research was to investigate an image analysis and classification techniques for detection and severity rating of olive leaf spot disease. Samples of olive leaves were collected
Features selection of cotton disease leaves image based on fuzzy feature selection techniques
TLDR
The results show that the effectiveness of features selected by the FC and FS method is much better than that selected by human randomly or other methods.
...
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