Author : Revathy P, Nandhini S, Nivetha G R
Page No: 19-27
Abstract : Outbreaks of plant diseases will result in deduction of agricultural products. If plant diseases are not discovered soon, there will be more food instability. In the agricultural world, automation in identification of plant diseases based on plant leaves is a big milestone. Quick and accurate detection of plant leaf disease is essential and has a positive impact on agricultural productivity and quality. Traditional classical approaches to detect a plant disease frequently appear powerless at this moment. It is very challenging to create such automatic algorithms that detect plant diseases by optically seeing symptoms on plant leaves. With the development of automated solutions, it is now simpler to properly identify disease which helps to decrease the wastage of crops due to disease. The main objective of this project is to introduce a mechanism which will identify and prevent the crop diseases as soon as possible. It helps to strengthen the objectivity of plant leaf disease feature extraction and minimize the limitations of intentionally selecting disease spot features. The paper explains about process and algorithm we used for the accuracy enhancement of our project using Machine Learning through feature extraction and classification which is done using dilated CNN. The large dataset collected from various sources was divided into testing and training images. The accuracy of the system is measured and used for prediction in our application.
Keyword plant disease, dilated CNN, prediction, accuracy, classification, neural networks

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