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 view of the fact that $\ensuremath{\alpha}$ decay and cluster radioactivity are physically analogical processes, we propose a general formula of half-lives and decay energies for $\ensuremath{\alpha}$ decay and cluster radioactivity. This new formula is directly deduced from the WKB barrier penetration probability with some approximations. It is not only simple in form and easy to see the physical meanings but also shows excellent agreement with the experimental values. Moreover, the difference between two sets of parameters to separately describe $\ensuremath{\alpha}$ decay and cluster radioactivity is small. Therefore, we use only one set of adjustable parameters to simultaneously describe the $\ensuremath{\alpha}$ decay and cluster radioactivity data for even-even nuclei. The results are also satisfactory. This indicates that this formula successfully combines the phenomenological laws of $\ensuremath{\alpha}$ decay and cluster radioactivity. We expect it to be a significant step toward a unified phenomenological law of $\ensuremath{\alpha}$ decay and cluster radioactivity.

Unified formula of half-lives for α decay and cluster radioactivity

Agriculture is the most primary and indispensable source to furnish national income of numerous countries including India. Diseases in plants/crops are the serious causes in degrading the production quantity and quality, which results in economy losses. Thus, identification of the diseases in plants is very important. Plant disease symptoms are evident in various parts of plants. However, plant leaves are most commonly used to detect the infection. Computer vision and soft computing techniques are utilized by several researchers to automate the detection of plant diseases using leaf images. Various aspects of such studies with their merits and demerits are summarized in this work. Common infections along with the research landscape at different stages of such detection systems are discussed. The modern feature extraction techniques are analyzed for identifying those that appear to work well covering several crop categories. The study would help the researchers to understand the applicability of computer vision in plant disease detection/classification.

Plant disease detection using computational intelligence and image processing

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Experimental data of complex cluster radioactivity $(^{14}\mathrm{C}\text{--}^{34}\mathrm{Si})$ are systematically analyzed and investigated with different models. The half-lives of cluster radioactivity are well reproduced by a new formula between half-lives and decay energies and by a microscopic density-dependent cluster model with the renormalized M3Y nucleon-nucleon interaction. The formula can be considered as a natural extension of both the Geiger-Nuttall law and the Viola-Seaborg formula from simple $\ensuremath{\alpha}$ decay to complex cluster radioactivity where different kinds of clusters are emitted. It is useful for experimentalists to analyze the data of cluster radioactivity. A new linear relationship between the decay energy of cluster radioactivity and the number of $\ensuremath{\alpha}$ particles in the cluster is found where the increase of decay energy for an extra $\ensuremath{\alpha}$ particle is between 15 and 17 MeV. The possible physics behind this new linear relationship is discussed.

New perspective on complex cluster radioactivity of heavy nuclei

The present study is based on the image processing techniques to identify and classify fungal rust disease of Pea. Rust disease is caused by Uromyces fabae (Pers.) de Bary in the form of rust-colored pustules on the leaves. The plant disease detection is limited by human visual capabilities due to microscopic symptoms of the disease and for that image processing techniques seems to be well adapted. The goal of this paper is to detect, to identify the early symptoms of rust disease at the microscopic level. The performance of various preprocessing, feature extraction and classification techniques was evaluated on microscopic images. Finally support vector machine classifier was used to detect the leaf disease of Pea Plant. The proposed system can successfully detect and examined disease with accuracy of 89.60%. Focus has been done on the early detection of rust disease at microscopic level which avoids spreading of disease not only on the whole plant but also to the other plants.

Support vector machine classifier based detection of fungal rust disease in Pea Plant ( Pisam sativam )

Cold fission of radioactive nuclei is studied in the super-asymmetric mass region of exotic cluster radioactivity, using the quantum-mechanical fragmentation theory (QMFT) based saddle-point fission (SPF) model for calculating the decay half-life times. The calculations show that cold fission also prefers light fragments like 24–26Ne, 28,30Mg, 32,34,36Si, 37P 38S, 46Ar and 48,50Ca, some of which are observed in exotic cluster radioactivity. The predictions of the SPF model calculations are compared with the available exotic cluster-decay experimental data and the calculations based on the preformed-cluster model (PCM). The SPF model calculations show large disagreements with both the PCM and cluster-decay experimental data. For clusters of mass A2 46. Also, cold fission is found to be more probable than hot fission and a new fission mode (known as bimodel fission) is identified in the neighbourhood of the doubly magic fragment. Our calculations are made for 234U, 238Pu, 241Am and 252Cf.

Super-Asymmetric Cold Fission and Exotic Cluster-Decay

Accurate and early detection of plant diseases will facilitate mitigate the worldwide losses experienced by the agriculture area. MATLAB image processing provides quick and non-destructive means of rust disease detection. In this paper, microscopic image data of rust disease of Lentil was combined with image processing with depth information and developed a machine learning system to detect rust disease at early stage infected with fungus Uromyces fabae (Pers) de Bary. A novel feature set was extracted from the image data using local binary pattern (LBP) and HBBP (Brightness Bi-Histogram Equalization) for image enhancement. It was observed that by combining these, the accuracy of detection of the diseased plants at microscopic level was significantly improved. In addition, we showed that our novel feature set was capable of identifying rust disease at haustorium stage without spreading of disease.

/pdf/automatic-detection-of-rust-disease-of-lentil-by-machine-y4kdypgr89.pdf

Automatic detection of rust disease of Lentil by machine learning system using microscopic images

Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e.  late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.

http://ijece.iaescore.com/index.php/IJECE/article/download/6267/6089

Recognition of Tomato Late Blight by using DWT and Component Analysis

Nowadays Wireless local area networks (WLANs) are growing very rapidly. Due to the popularity of 802.11 networks, possibilities of various attacks to the wireless network have also increased. In this paper, a special type of attack De-Authentication/disassociation attack has been investigated. In a normal scenario, a wireless client or user sends a de-authentication frame when it wants to terminate the connection. These frames are in plain text and are not encrypted. These are not authenticated by the access point. Attackers take advantage of this, and spoof these packets and disable the communication between the connected client and access point. In this paper, an algorithm based on radio-tap header information is suggested to identify whether there is a De-Authentication attack on the client or not.

/pdf/a-light-weight-solution-for-detecting-de-authentication-llswdqri11.pdf

A Light Weight Solution for Detecting De-Authentication Attack

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