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

In traditional practices, detection of rice plant diseases by experts is a subjective matter whereas by testing in the laboratory is time-consuming. As a consequence, it causes reduction on agricultural production and economic loss to farmers. To overcome this, there is a demand to develop fast and effective systems to detect and classify rice plant diseases. Therefore, the development of image-based automated systems for classification of rice plant diseases is an interesting growing research area in the agriculture domain. Color is one of the important features to classify rice plant diseases. In this study, we have presented an image-based rice plant disease classification approach using color features only. We have explored 14 different color spaces and extracted four features from each color channel leading to 172 features. Moreover, the performance of seven different classifiers have been compared and demonstrated that a highest classification accuracy of 94.65% has been achieved using support vector machine (SVM) classifier. Training and testing of models were performed on the dataset that consists of 619 images. This dataset was collected from the real agriculture field that belongs to four classes: (a) Bacterial Leaf Blight (BLB), (b) Rice Blast (RB), (c) Sheath Blight (SB) and (d) Healthy Leave (HL). The encouraging results of this paper show that color features can play an important role in developing rice plant disease classification system and enable the farmers to take preventive measures resulting in better product quality and quantity.

Rice plant disease classification using color features: a machine learning paradigm

An approach based on digital image analysis to estimate the live weights of pigs in farm environments

Automatic identification of diseases in grains crops through computational approaches: A review

Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams is also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and can also seek in into your system. Creating a fake profile and email account is much easy for the spammers, they pretend like a genuine person in their spam emails, these spammers target those peoples who are not aware about these frauds. So, it is needed to Identify those spam mails which are fraud, this project will identify those spam by using techniques of machine learning, this paper will discuss the machine learning algorithms and apply all these algorithm on our data sets and best algorithm is selected for the email spam detection having best precision and accuracy.

Email Spam Detection Using Machine Learning Algorithms

The disease infliction of the plants severely influences the yield. It alters the essence and extent of crop production cause fiscal distress. Consequently, the diagnosis of numerous plant diseases is significant to decrease the yield perdition by discovering crop infections at their earlier stages. This paper introduces a new model using mobile video image processing and Long-Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability. The rice plant videos captured under uncontrolled conditions in day-lighting using a mobile handset and divided into two sections for the designing and testing of LSTM-SRNN models. After shooting, the video images of the rice plant segmented using colour indexing and linear color space transformation with minimal daylight impact. Low-level spatial features; entropy, standard deviation, and fuzzy features extracted after video image segmentation. The excerpted characteristics with the composite combinations transformed in time-series datasets with the desired response. The datasets employed in the LSTM-SRNN model for progressive learning. The distinct test video features applied in LSTM-SRNN to appraise the generalization capability of the proposed system with performance analysis. The experimental outcomes of the proposed LSTM-SRNN model exhibit 99.99% prediction ability with fuzzy features. The model also presents possibilities for dynamic learning adaptability and temporal information processing to overcome the limitations of many well-known rule-based and machine learning approaches.

Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study

Today emails have become to be a standout amongst the most well-known and efficient types of correspondence for Internet clients. Hence because of its fame, the email will be misused. One such misuse is the posting of unwelcome, undesirable messages known as spam or junk messages. Email spam has different consequences. It diminishes productivity, consumes additional space in mail boxes, additional time, expand programming damaging viruses, and materials that contains conceivably destructive data for Internet clients, destroy stability of mail servers, and subsequently clients invest lots of time for sorting approaching mail and erasing undesirable correspondence. So there is a need of spam detection so that its outcomes can be reduced. In this paper, propose a novel method for email spam detection using SVM and feature extraction which achieves accuracy of 98% with the test datasets.

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E-Mail Spam Detection and Classfication Using SVM and Feature Extraction

This Event log file is the most common data-sets exploited by many companies for customer behavior analysis. Oftentimes these records are unordered, and need to be grouped by certain key for effective analysis. One such example is to group similar user with different session ID to facilitate further analysis. This kind of analysis is known as User Sessionization. In this paper, we propose a distributed framework in combination of Hadoop and MapReduce to analyze event log file and sessionize user based on IP-address and timestamp. The evaluation results show that as the number of nodes increases the execution time decreases and performance increases.

A distributed framework for event log analysis using MapReduce

Currently, researchers are developing numerous plant diseases recognition model using image processing and soft computing. The models are mainly based on the extraction of discolored features and applying in various soft computing approaches to automate plant diseases recognition process. The extracted features are statistical, frequency, spatial-frequency or hybrid features of captured images accessed in device-dependent or device-independent color spaces. The performance of diseases recognition system is significantly dependent upon the selection of color spaces and extracted features. This paper presents a comprehensive review of the impact of color spaces and feature sets on machine learning and rule base automated plant diseases classifier. The review performed with six categories of rice plant images with two machine learning and two rule base classifiers. Initially, a thorough literature review performed on the previous investigation based on color spaces and used feature sets for designing diseases recognition model. Then common conditions created to extract feature sets in different color spaces, and applied machine learning and rule base classifier to analyze the impact of color spaces with feature sets. The review presents a detailed discussion on the correlation between color spaces, feature sets, and performance of diseases recognition system. The review results reveal the most relevant features on specific color space for machine learning and rule base classifier. It also deduces that the performance of plant diseases classifier highly dependent upon used color space and extracted features.

Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

The availability of digital data such as images, audio and videos to the public is growing day by day with the expansion of the Internet. Hence preserving the rightfulness of the data is a critical factor and the solution is effectively provided by digital watermarking. Digital watermarking is a developing technology which ensures and facilitates security, authentication and copyright protection of digital data. We tries to propose a secure and robust watermarking algorithm based on the combination of image interlacing, DWT & DCT techniques. To minimize the bandwidth requirement during transmission of watermarked image EBCOT algorithm to compress the image and error correcting codes are applied to receive error free content at receiver end.

Toran Verma

Papers

Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study

E-Mail Spam Detection and Classfication Using SVM and Feature Extraction

A distributed framework for event log analysis using MapReduce

Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

A new digital image watermarking algorithm based on image interlacing, DWT, DCT