Agricultural productivity is that thing on which the Indian Economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agriculture field, as having the disease in plants is 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 the 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 a survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect of disease detection in plant leaf disease, is done by using Neural Network.
Indian economy is dependent on agricultural productivity. Over 70% of rural homes depend on agriculture. Agriculture pays about 17% of 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, and wheat. Indian farmers also grow sugarcane, oilseeds, potatoes, and non-food items like coffee, tea, cotton, and rubber. All these crops grow based on strength of leaves and roots. There are things that lead to different diseases for the plant leaves, which spoiled crops, and finally, it will affect the economy of the country.
- Principal Component Analysis
- Region-based segmentation
- KNN classifier
DRAWBACKS ON EXISTING:
- High Computational load
- Poor discriminatory power
- Less accuracy in classification
- Feature extraction of glam
- convolution neural network
- Threshold segmentation
It easily identifies the disease by using a convolution neural network.
- 4 GB of RAM
- 500 GB of Hard disk
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