Identification of rice diseases using deep convolutional neural networks

Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discussed the methods used for the detection of plant diseases using their leaves images. This paper also discussed some segmentation and feature extraction algorithm used in the plant disease detection.

Plant Disease Detection Using Image Processing

A review on the main challenges in automatic plant disease identification based on visible range images

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.

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Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

The advancement of Mobile Cloud Computing has increased the number of users that are using various cloud services to execute resource hungry applications. Thus more number of more third party infrastructure are provisioned on demand to meet the ever increasing demand of mobile cloud users. With several third party infrastructure coming to business makes the cloud network susceptible to various network attacks. Thus security in mobile cloud computing is a sheer necessity so that users can avail various cloud services seamlessly. In this work an automated fuzzy based expert system is proposed that perform forensic analysis on cloud network log to identify the attack. Further the proposed expert system is tested in our private cloud infrastructure. The result shows improved accuracy and improved precision in identifying attacks compared to other standard forensic system

Forensics-as-a-Service for Mobile Cloud Environment

ABID: Attention-Based Bengali Image Description

Cloud computing is one of the most recent advancements in the field of distributed computing. It has gained a lot of attention due to its on demand, pay-per-use service, and all time availability, reliability, and scalability. Although it offers numerous advantages, but due to its multi-tenant architecture, it is prone to various malicious attacks and illegal activities. Cloud service provider (CSP) takes the responsibility to secure customers’ data against such attacks. In the event of such malicious activities, CSP aims to trace the intruder. Cloud forensic techniques help in identifying the attacker along with proper evidence in cloud platform. Components of clouds such as log records are then analyzed to track for such detrimental activities. In this paper, some existing log-based cloud forensic techniques have been widely studied. The detailed comparative analysis has been done for the various techniques based on their advantages and limitations. By exploring the limitations and advantages of the existing approaches, future research areas have been identified.

Log-Based Cloud Forensic Techniques: A Comparative Study

The task of automatically identifying the used language from speech signals is known as automatic language identification. It is very much important prior to speech recognition in multilingual scenarios where speakers use more than a single language in course of communication. In this paper, a recurrent neural network (RNN)-based system with long short-term memory (LSTM) along with handcrafted line spectral frequency-based features is proposed for language identification. Experiments were performed on as many as 21908 clips (more than 30 h of data) from the top three spoken languages of the world, namely, English, Chinese, and Spanish, and a highest average accuracy of 95.22% has been obtained.

A Recurrent Neural Network-Based Approach to Automatic Language Identification from Speech

In this paper a technique for automatic segmentation of spoken word signals is presented for identifying letters for transcription into textual form. Signal patterns for each letter present in different words have been used for the purpose. Voice signals are obtained by taking pronunciations of 1,000 words available in the standard dictionary. After collecting the signals, pre-processing is performed to reduce the noise taking a heuristically determined threshold value. Then the signals are segmented based on Amplitude Variation (AV) in different portions of the signal, each corresponding to an alphabet in that particular word. Signal Peak Value (SPV) is the feature used for recognizing the letters. Accuracy of the method is estimated using Bagging, Bayes Net, J48, Naive Bayes, PART and SVM classifiers available in Weka. The best and the average classification accuracies obtained in this method are 95.15 % (given by J48 classifier) and 86.92 %, respectively, which are quite acceptable.

Santanu Phadikar

Papers

Forensics-as-a-Service for Mobile Cloud Environment

ABID: Attention-Based Bengali Image Description

Log-Based Cloud Forensic Techniques: A Comparative Study

A Recurrent Neural Network-Based Approach to Automatic Language Identification from Speech

Automatic Segmentation of Spoken Word Signals into Letters Based on Amplitude Variation for Speech to Text Transcription