Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight (Exserohilum), common rust (Puccinia sorghi) and gray leaf spot (Cercospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

In recent years hyperchaos has found applications in several fields such as medicine and engineering. It has been applied in image-based applications which require reliable, fast, and robust security systems to store or/and transfer images. Given the increased use of images, there is a fundamental requirement among security in ensuring protection, confidentiality, privacy, integrity, and authenticity of such images. This, in essence, has become a major concern. Many preventive and protective algorithms, including chaos-based schemes, have been developed over the past years in trying to prevent directed attacks. However, many existing chaos-based encryption schemes have been successfully decrypted. One of the main reasons for these chaos-based image encryption schemes being successfully attacked is that the degree of randomness of the chaotic system used is not high enough to guarantee robust encryption. The key space lengths of many of the existing cryptosystems are not long enough to guarantee the desired security properties of chaos-based ciphers. Thus, the security level is lowered. In this paper, a selective image encryption algorithm based on 2D Discrete Wavelet Transform, Henon's Map and 4D Qi Hyper Chaos is proposed. This paper is developed such that 2D DWT is used to decompose the image into details and approximations, Henon chaotic map is used to shuffle the decomposed image pixels' positions. The shuffled image is further encrypted with the bit streams generated by Qi hyperchaos using XOR operation. The experimental results demonstrate that the proposed algorithm provides a large key space, high security, and resistance to different types of attacks. When compared to some existing algorithms, the performance of the proposed method displays a better performance with respect to security and encryption making it suited for real-time applications.

This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.

The world is already on the way of connecting everything (objects) to the internet, from refrigerators to sprinkler systems. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. The internet of things within the broader domains of security also requires algorithms that are secure and are able to protect the gadgets connected to the internet. There are problems concerning security, especially on the algorithms used on the IoT. This study, we managed to; (1) Collecting existing block cryptographic algorithms (maximum of ten) for authentication and encryption from different devices used on Internet of Things; (2) Analyze their avalanche effect and speed of collected cryptographic algorithms from step one if they give efficient authentication on Internet of Things; (3) Improving their speed and avalanche effect by designing a mathematical model that strengthens the avalanche effect of the algorithms. This was done by using an initial vector XORed with plaintext and final vector XORed with cipher text (4) Testing the new mathematical model if it really enhances the avalanche effect and speed on each and every algorithm tested in step one.

Plant leaf diseases can affect the plants’ leaves to an extent that the plants can collapse and die completely. These diseases may drastically drop the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the Convolutional Neural Networks (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognized and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and the methodology used, expedite a quick and easy implementation of the system in practice. The developed model was able to recognize 3 different types of maize leaf diseases out of healthy leaves. The Northern Corn Leaf Blight (Exserohilum), Common Rust (Puccinia sorghi) and Gray Leaf Spot (Cerospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.