8Paddy Leaf Classification Using Computational Intelligence
S. Vidivelli*, P. Padmakumari and P. Shanthi
School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India
Abstract
Plant diseases have been a significant problem for farmers, causing severe damage to crops and resulting in economic losses. The detection and classification of plant diseases are essential for maintaining a healthy crop yield and reducing the use of pesticides, which can harm the environment and human health. In this chapter, we propose a framework for the classification of rice leaf diseases using our proposed Local Binary Pattern and Fractal features, extracted from digital images of diseased leaves. To achieve the goal of efficient and accurate disease detection, we utilize modern technologies such as image processing and machine learning techniques. Our proposed framework uses an Adaboost ensemble classifier for classification, which has shown to be effective in several applications. The framework is tested on a publicly available dataset of rice leaf images, and the results show that our proposed method outperforms other state-of-the-art methods in terms of accuracy.
Keywords: Fractal dimension, paddy, Adaboost, adaptive thresholding, support vector machine
8.1 Introduction
Agriculture is a critical driving force for many countries’ economies, and its success is largely dependent on environmental factors such as weather and the quality of crops. In modern agriculture, the use of technologies ...