Several studies have invested in machine learning classifiers to protect plants from diseases by processing leaf images. Most of the proposed classifiers are trained and evaluated with small datase...

Deep Learning for Tomato Diseases: Classification and Symptoms Visualization

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep-CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80% mAP on ALDD, with a high-detection speed of 23.13 FPS. The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.

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Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. This paper discusses various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. This paper outlines various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. This paper explains the importance of introducing smart meters in developing countries. In addition, the status of smart metering in various countries is also illustrated.

Smart meters for power grid — Challenges, issues, advantages and status

International Geoscience and Remote Sensing Symposium (IGARSS)

The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images Several works utilized computer vision technologies effectively and contributed a lot in this domain This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions The survey would help researchers to gain understanding of computer vision applications in plant disease detection

Plants Disease Identification and Classification Through Leaf Images: A Survey

The major cause for decrease in the quality and amount of agricultural productivity is plant diseases. Farmers encounter great difficulties in detecting and controlling plant diseases. Thus, it is of great importance to diagnose the plant diseases at early stages so that appropriate and timely action can be taken by the farmers to avoid further losses. The project focuses on the approach based on image processing for detection of diseases of soybean plants. The soybean images are captured using mobile camera having resolution greater than 2 mega pixels. The purpose of the proposed project is to provide inputs for the Decision Support System (DSS), which is developed for providing advice to the farmers as and when require over mobile internet. Our proposed work classifies the images of soybean leaves as healthy and diseased using Support Vector Machine (SVM). The algorithm comprises of four major steps: image acquisition, extracting the leaf from complex background, statistical analysis and classification. The pre-processing step includes conversion from RGB to HSV (Hue Saturation Value) color space. For extracting the region of interest (ROI) from the original image, multi-thresholding is used. The color based and cluster based methods are used for segmentation. The algorithm uses Scale Invariant Feature Transform (SIFT) technique which automatically recognizes the plant species based on the leaf shape. The SVM classifier proves its ability in automatic and accurate classification of images. Finally, it can be concluded from the experimental results that this approach can classify the leaves with an average accuracy of 93.79%. The proposed system will enable the farmers to get advice from the agricultural experts with minimal efforts.

An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective

Plant diseases cause major economic and production losses as well as curtailment in both quantity and quality of agricultural production. Now a day's, for supervising large field of crops there is been increased demand for plant leaf disease detection system. The critical issue here is to monitor the health of the plants and detection of the respective diseases. Studies show that most of the plant disease can be diagnosed from the properties of the leaf. Thus leaf based disease analysis for plants is an exciting new domain. The technique proposed for identification of plant disease through the leaf texture analysis and pattern recognition. In this work we focus on Grapes plant leaf disease detection system. The system takes a single leaf of a plant as an input and segmentation is performed after background removal. The segmented leaf image is then analyzed through high pass filter to detect the diseased part of the leaf. The segmented leaf texture is retrieved using unique fractal based texture feature. Fractal based features are locally invariant in nature and therefore provides a good texture model. The texture of every independent disease will be different. The extracted texture pattern is then classified using multiclass SVM. The work classifies focus on major diseases commonly observed in Grapes plant which are downy mildew & black rot. The proposed approach avails advice of agricultural experts easily to farmers with the accuracy of 96.6%.

Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System

Removing prediction lag in wave height forecasting using Neuro - Wavelet modeling technique

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data. Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications. In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

Image and Video Compression: Fundamentals, Techniques, and Applications

Security is main concern in Automated Teller Machines today. Multimodal Biometrics are more secure as compared to Unimodal Biometrics as even if one trait fails the other is present. The futuristic application of Multimodal Biometrics could be in Automated Teller Machines (ATM). The proposed work involves capturing of three biometric traits of a person namely face, fingerprint and palm vein by designed hardware later these are preprocessed and fused together for cryptography. Palm vein is chosen as a biometric trait as no two palm veins match unless they are of the same person also palm has a good vascular pattern making it a good identifying factor for an individual as compared to other biometric traits. Further security is achieved in the system by fusion of palm vein with face and fingerprint. Cryptosystem is used in order to get security. The images captured by the designed hardware are preprocessed using Image enhancement techniques and Features are extracted by Curvelet Transform, Gabor Filter and Principal Component analysis. The feature vectors are fused at feature level using Euclidean distance and later matched.

Yogesh H. Dandawate

Papers

An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective

Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System

Removing prediction lag in wave height forecasting using Neuro - Wavelet modeling technique

Image and Video Compression: Fundamentals, Techniques, and Applications

Fusion based Multimodal Biometric cryptosystem