Agricultural productivity is that thing on which Indian Economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases means when they appear on plant leaves. This paper presents an algorithm for image segmentation technique used for automatic detection as well as classification of plant leaf diseases and survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using Neural Network.
Indian economy is dependent of agricultural productivity. Over 70% of rural homes depend on agriculture. Agriculture pays about 17% to the total GDP and provides employment to over 60% of the population. Therefore detection of plant diseases plays a vital key role in the arena of agriculture. Indian agriculture is composed of many crops like rice, wheat. Indian farmers also grow sugarcane, oilseeds, potatoes and non-food items like coffee, tea, cotton, rubber. All these crops grow based on strength of leaves and roots. There are things that lead to different disease for the plant leaves, which spoiled crops and finally it will effect on economy of the country.
?? Existing Systems
- Principal Component Analysis
- Region based segmentation
- KNN classifier
DRAW BACKS ON EXISTING:
- High Computational load?
- Poor discriminatory power
- Less accuracy in classification
- Feature extraction of glcm
- convolution neural network
- Threshold segmentation
It is easily identify the disease by using convolution neural netwok
Digital image processing deals with?manipulation of digital images through a digital computer. It is a subfield of signals and systems but focus particularly on images. DIP focuses on developing a computer system that is able to perform?processing?on an?image. The input of that system is a digital?image?and the system process that?image?using efficient algorithm
?It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing
- Importing the image via image acquisition tools;
- Analysing and manipulating the image;
- Output in which result can be altered image
Image Pre-processing?is a common name for operations with?images?at the lowest level of abstraction. Its input and output are intensity?images.? The aim of?pre-processing?is an improvement of the?image?data that suppresses unwanted distortions or enhances some image?features important for further processing.
To create a GLCM, use the?graycomatrix?function. The?graycomatrix?function creates a gray-level co-occurrence matrix (GLCM) by calculating how often a pixel with the intensity (gray-level) value?i?occurs in a specific spatial relationship to a pixel with the value?j. By default, the spatial relationship is defined as the pixel of interest and the pixel to its immediate right (horizontally adjacent), but you can specify other spatial relationships between the two pixels. Each element (i,j) in the resultant?GLCM is simply the sum of the number of times that the pixel with value?i?occurred in the specified spatial relationship to a pixel with value?j?in the input image. Because the processing required to calculate a GLCM for the full dynamic range of an image is prohibitive,?graycomatrix?scales the input image. By default,?graycomatrix?uses scaling to reduce the number of intensity values in gray scale image from 256 to eight. The number of gray levels determines the size of the GLCM. To control the number of gray levels in the GLCM and the scaling of intensity values, using the?Num Levels?and the?Gray Limits parameters of the?graycomatrix?function. See the?graycomatrix?reference page for more information
Discrete Wavelet Transform (DWT)
The discrete wavelet remodel (DWT) became superior to use the wavelet rework to the digital international. Filter banks are used to approximate the behavior of the non-prevent wavelet remodel. The sign is decomposed with a immoderate-skip smooth out and a low-bypass clear out. The coefficients of these filters are computed using mathematical evaluation and made to be had to you. See Appendix B for more records about those computations.
2.2 Discrete Wavelet Transform
LP d: Low Pass Decomposition Filter
HP d: High Pass Decomposition Filter
LP r: Low Pass Reconstruction Filter
HP r: High Pass Reconstruction Filter
- 4 GB of RAM
- 500 GB of Hard disk
- MATLAB 2018b
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