Varieties Classification into Plain, Patterned and Un-patterned from Fabric Images

The presented work gives a methodology to classify fabric images as plain, patterned and un-patterned. Discrete Wavelet Transform is applied and wavelet features are extracted. Feed Backward Selection Technique is used in the feature selection phase. Two prediction models, namely, Support Vector Machine and Artificial Neural Network are used. The overall classification rates of 81% and 86.5% are obtained for fabric types using Support Vector Machine and Artificial Neural Network classifiers. The classification rates for varieties of non-plain fabric images are found to be 84% and 88% respectively

Automated Classification of Commonly used Hybrid and Non-Hybrid Vegetables

The proposed work classifies commonly used hybrid and non-hybrid vegetables. The number of hybrid vegetables is more than the number of non-hybrid ones. Consumption of hybrid food for longer period can lead to various types of cancers due to lack of nutrients. People are increasingly becoming health conscious and thus most of the population started preferring non-hybrid food items. In the proposed work, four types of regularly consumed vegetables in the food items namely, brinjal, tomato, carrot and cucumber. Differentiating hybrid and non-hybrid vegetables in market is tough job for most of the people, especially, for the people living in cities. Thus, we propose to develop an application which will be able to identify and classify the vegetable image into hybrid or non-hybrid. The overall classification rate for classifying hybrid and non-hybrid vegetable images is 92% and 96.75%

Vision Based Classification of Different Diseases of Grape Leaves and their Severity

Grape leaf diseases are one of the most important reasons that lead to the destruction of grape fruits. The annual worldwide yield losses due to pests are estimated to be billions of dollars.  Integrated pest management (IPM) is one of the most important components of crop production in most agricultural areas of the world, and the effectiveness of crop protection depends on accurate and timely diagnosis of phytosanitary problems. Detecting those diseases at early stages enable us to overcome and treat them appropriately. Here, we are classifying grape leaf images as healthy or diseased. The diseased leaf image is classified into various types along with their severity. This is carried out with the help of digital image processing which involves image analysis, visual examination and inspection of colo

Computer Vision Based Identification of Dengue Mosquitoes from Images

The proposed work detects and identifies the Dengue mosquito from the images based on its xdescriptor values. Dengue mosquito is a carrier of dengue virus which causes the hemorrhagic fever. The image is identified as dengue or normal mosquito image. Further, the dengue mosquito image is identified for a male dengue mosquito or female dengue mosquito.  The descriptor values of size, stripes on legs, slender body and color are extracted. The accuracy of identification of mosquito and other insects is found to be 98%. The accuracy of identification of dengue mosquito and other mosquito is found to be 97%. Similarly, accuracy of male and female mosquitoes is found to be 98.5%

Comparative Analysis of Rule Based and K-Nearest Neighbour Approaches for Classification of Different Shades of Basic Colours in Fabric Images

Proposed work presents comparative analysis of rule based and K-nearest neighbour approaches to classify the different shades of basic colours in fabric images. The mean and standard deviation of CIE Lab features of shades of red, green and blue colours are computed. A rule base is designed. An overall recognition rate of 97% is obtained in case of rule based approach. K-nearest neighbour classifier is designed taking into account, the average Lab values. The overall recognition rate of 94% is obtained in case of K- nearest neighbour approach

A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

The agriculture sector is no exception to the widespread usage of deep learning tools and techniques. In this paper, an automated detection method on the basis of pre-trained Convolutional Neural Network (CNN) models is proposed to identify and classify paddy crop biotic stresses from the field images. The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely, Inception-V3, VGG-16, ResNet-50, DenseNet-121 and MobileNet-28. Brown spot, hispa, and leaf blast, three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation. The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61% outperforming the other considered CNN models. The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts