Control of diseases affecting plants is very important as it relates to the issue of food security, which is a very serious threat on human life. According to the International Maize and Wheat Improvement Center (CIMMYT), most of the maize diseases are caused by mildew. There are more than fifty different mildew diseases affecting maize. The diseases that infect plants go through different stages of the degree of infection. Therefore, determining the degree of injury helps decision-makers spend money on controlling the disease properly. The common method used to determine the degree of injury is visual examination. It’s known that visual examination leaves a large area for error. It is unable to determine the degree of injury with high accuracy as a percentage for example. In this study, we used two methods of segmentation to determine the injury ratio in maize leaves. The methodology of segmentation depends on the Color Threshold Application (CTA) and K-mean clustering. For comparison between methods of segmentation, different color spaces (RGB, HSV, YCbCr, and L*a*b*) have been used. The results obtained indicate that the color threshold segmentation is more efficient than K-means clustering in terms of determining the injury ratio, especially with HSV color space. While it contrary to the RGB color model because it gives the worst result.

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The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency.

Information security, being one of the corner stones of network and communication technology, has been evolving tremendously to cope with the parallel evolution of network security threats. Hence, cipher algorithms in the core of the information security process have more crucial role to play here, with continuous need for new and unorthodox designs to meet the increasing complexity of the applications environment that keep offering challenges to the current existing cipher algorithms. The aim of this review is to present symmetric cipher main components, the modern and lightweight symmetric cipher algorithms design based on the components that utilized in cipher design, highlighting the effect of each component and the essential component among them, how the modern cipher has modified to lightweight cipher by reducing the number and size of these components, clarify how these components give the strength for symmetric cipher versus asymmetric of cipher. Moreover, a new classification of cryptography algorithms to four categories based on four factors is presented. Finally, some modern and lightweight symmetric cipher algorithms are selected, presented with a comparison between them according to their components by taking into considerations the components impact on security, performance, and resource requirements.

Advances in technology are increasing the demand for the biometric technology of palm print detection. This work presents a region of interest segmentation algorithm for palm prints based on Hue Saturation Brightness. The aim of this work was to detect palm print region of interest based on skin color and geometric features. After a color palm image is read as a JPEG file, the algorithm passes through three main stages of preprocessing: palm localization, region of interest localization and region of interest extraction. The preprocessing stage consists of three steps: conversion to Hue Saturation Brightness, skin color modeling, and de-noising. The second main stage of palm localization consists of clipping from the left, from the right, from the top and from bottom. The third stage consists of the two steps of determining and extracting region of interest. The algorithm was tested on a dataset of 180 palm images from the Institute of Automation Chinese Academy of Sciences dataset, which contains an equal number of right-hand and left-hand palm images. The results showed that the Hue Saturation Brightness color space for skin detection was superior to other recognition methods, with an accuracy rate of 98.8%.