SVM-based Classifier System for Recognition of Cotton Leaf Diseases
نویسنده
چکیده
The allocation and recognition of cotton leaf diseases are of the major importance as they have a cogent and momentous impact on quality and production of cotton. This paper presents a modus operandi for automatic classification of cotton leaf diseases through feature extraction of leaf symptoms from digital images. Otsu’s segmentation method is used for extracting color and shape features. Support vector machines (SVM) had been used to do classification on the extracted features. Three diseases have been diagnosed, namely Bacterial Blight, Myrothecium and Alternaria. The testing samples of the images are gathered from CICR Nagpur, cotton fields in Buldhana & Wardha district.
منابع مشابه
Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases
Wheat industry is an important constituent of Northern China’s overall agricultural economy. Proper disease detection using computer vision and pattern recognition has being investigated to minimize the loss, and finally achieve intelligent healthy farming. This paper proposes a new strategy of Multi-Classifier System based on SVM (support vector machine) for pattern recognition of wheat leaf d...
متن کاملObject Recognition based on Local Steering Kernel and SVM
The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...
متن کاملClassification of Cotton Diseases Using Cross Information Gain_minimal Resource Allocation Network Classifier with Particle Swarm Optimization
This paper is developed based on machine vision system and data mining techniques to identify the cotton leaf spot diseases. The leaves are most probably affected by the fungi, viral and bacterial diseases in the leaf spot areas which plays a vital role of crop situation. This paper clarifies six types of diseases in the cotton plant. The significance of this research work design is based on ad...
متن کاملRecognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier
This paper presents a new framework based on modified EMD method for detection of single and multiple PQ issues. In modified EMD, DWT precedes traditional EMD process. This scheme makes EMD better by eliminating the mode mixing problem. This is a two step algorithm; in the first step, input PQ signal is decomposed in low and high frequency components using DWT. In the second stage, the low freq...
متن کاملA COMPARATIVE ANALYSIS OF WAVELET-BASED FEMG SIGNAL DENOISING WITH THRESHOLD FUNCTIONS AND FACIAL EXPRESSION CLASSIFICATION USING SVM AND LSSVM
This work presents a technique for the analysis of Facial Electromyogram signal activities to classify five different facial expressions for Computer-Muscle Interfacing applications. Facial Electromyogram (FEMG) is a technique for recording the asynchronous activation of neuronal inside the face muscles with non-invasive electrodes. FEMG pattern recognition is a difficult task for the researche...
متن کامل