A Framework for Crop Disease Detection Using Feature Fusion Method

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection

A Review on Advances in Automated Plant Disease Detection

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images

Character Segmentation Technique for Printed and Handwritten Devanagari Script without Extraction of Shirorekha

In India, there is a lot of literature available in the Devanagari script as well as Devanagari is most frequently used for written, oral correspondence and documentation reasons. How correctly the character segmentation of the Devanagari content is done, will ultimately decide the exactness of the OCR process. In this paper, we have proposed the character segmentation strategy along with existence of Shirorekha framed for printed as well as handwritten text written in Marathi language. Several methods utilized for pre-processing the document images like document binarization, skew identification and correction are discussed in this paper. We took vertical projection of the segmented words, compared the pixel count with the automatically calculated threshold and then characters were separated. With the proposed strategy, we have achieved 100 % exactness in line and word segmentation and results accomplished for character segmentation are a lot of encouraging. No standard dataset of Marathi characters is available having upper and lower modifiers. Non-availability of the standard datasets with modifiers is a significant issue in using a deep learning network for perceiving the Devanagari characters. The segmented characters with the presence of Shirorekha can be straightforwardly utilized for developing the deep learning OCR framework

Design of Deck Slab of Flyover

The project deals with deck slab of fly over bridge in Ahmednagar city. As the bridge keeps more importance for public transport and traffic management so the modern technique should be adopted for construction. In accordance with that we are designing deck slab with prestressed post tensioned technique. This first introduced in 1928 by Eugene Freyssinet , a french engineer. Post tensioned bridge deck are generally adopted for longer span exceeding 20 m. Long span continuous prestressed concrete bridges are invariably built of multicelled box girder segments of variable depth. This project checks the stability of deck by manually and with help of software and also facilitate us to go for better accuracy