A Brief Review on Plant Disease Detection Using Image Processing Techniques

The Indian economy is highly dependent on Agriculture productivity. Having diseases in plants are natural, so disease detection in plant plays an important role in agriculture field. If proper care is not taken, then it causes very serious effects on plants, so that respective product quality and product quantity is affected. Plant disease detection using automatic technique is very useful because it reduces a large work of monitoring in big farms. At very early stage itself it detects the symptoms of diseases when they appear on plant leaves. This project focuses on an approach based on image processing techniques to detect the disease of plants.

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A survey on various image processing techniques and machine learning models to detect, quantify and classify foliar plant disease

Proceedings of the Indian National Science Academy ◽

10.1007/s43538-021-00027-4 ◽

2021 ◽

Author(s):

Akruti Naik ◽

Hetal Thaker ◽

Dhaval Vyas

Keyword(s):

Machine Learning ◽

Image Processing ◽

Plant Disease ◽

Learning Models ◽

Image Processing Techniques ◽

Processing Techniques ◽

Machine Learning Models

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Application of Image Processing in Fruit and Vegetable Analysis: A Review

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0079 ◽

2015 ◽

Vol 24 (4) ◽

pp. 405-424 ◽

Cited By ~ 13

Author(s):

Shiv Ram Dubey ◽

Anand Singh Jalal

Keyword(s):

Image Processing ◽

Expert Knowledge ◽

State Of The Art ◽

Disease Classification ◽

Disease Detection ◽

Fruit And Vegetable ◽

Image Processing Techniques ◽

Agricultural Operations ◽

Fruit Disease ◽

Processing Techniques

AbstractImages are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed image-processing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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Detection of Cotton Leaf Disease Using Image Processing Techniques

Journal of Physics Conference Series ◽

10.1088/1742-6596/2062/1/012009 ◽

2021 ◽

Vol 2062 (1) ◽

pp. 012009

Author(s):

Sushreeta Tripathy

Keyword(s):

Image Processing ◽

Plant Disease ◽

Region Of Interest ◽

Texture Features ◽

Disease Diagnosis ◽

Cotton Leaf ◽

Diagnosis System ◽

Image Processing Techniques ◽

Plant Disease Diagnosis ◽

Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

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