Development of Tomato Septoria Leaf Spot and Tomato Mosaic Diseases Detection Device Using Raspberry Pi and Deep Convolutional Neural Networks

Machine learning techniques are revolutionizing multiple industries, various researches have been put forward as regards mitigating pest and disease effect on food production. The ability to identify plant disease on time can help reduce the level of destruction caused by the diseases. This paper proposes the use of Deep Convolutional Neural Network (DCNN) as classification technique using keras and tensorflow python machine learning libraries to build a model deployed on a hand-held raspberry pi device for on-site plant disease classification. Convolutional Neural Networks (CNN) can automatically recognize interesting areas in images which reduces the need for image processing, training images were gotten from plantvillage.org and split into training, testing and validation sets, the training images were augmented and fed into a DCNN model for training the model was then tested on the test set to check against overfitting before finally used to detect disease on the validation set which showed very positive results. Results from this research shows that DCNN and the framework in this paper can be used to develop highly efficient plant disease detection models

Evidence of Students’ Academic Performance at the Federal College of Education Asaba Nigeria: Mining Education Data

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student’s performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students’ summative performance at semester’s end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling

An Empirical Investigation on Adoption of Mobile Technology in Nigeria

The growth of mobile technology has brought about a paradigm shift in the way and manner businesses are transacted all over the world. The level of diffusion of ICTs has tremendously opened up other vistas of business transactions. The following platforms are commonplace in today’s business world: m-business/m-commerce, m-banking, m-news (online radio, online television and online newspapers), m-reservation (flight, hotels, etc.) and m-learning among others. This study adopted Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the adoption of seven mobile technology-based solutions in Nigeria. The research instrument was developed for the seven solutions (online shopping, online reservation, package tracking, mobile money, mobile banking, mobile news and location-based services) based on the existing items of UTAUT adapted from previous studies. The survey research was adopted with about 600 copies of the questionnaire administered in hard copy to residents in four states in Nigeria (Lagos, Ogun, Rivers and Oyo), and the Federal Capital Territory, Abuja. The data were analysed based on Structural Equation Modelling using SmartPLS 3.2.7. The result showed that there is variation in the factors that influence the intention to use and actual usage of the mobile services investigated. The result of this study has the potential to assist policy makers and developers

A Systematic Mapping Study of Cloud, Fog, and Edge/Mobile Devices Management, Hierarchy Models and Business Models

Cloud computing is an exceptional paradigm, which is facilitating the developments and utilization of resources over the internet. Fog computing operates at the edge of the network saving bandwidth, by not sending all information to the cloud, while edge computing does processing of data at the edge of the cloud. Edge computing reduces the distance data must travel on the network. The unique relationship between cloud, fog and edge computing makes research in these areas mandatory. Deciding on a specific area of research as regards these subjects could be a bulky procedure for a scientist. Therefore, reviews and paper studies for recognizing potential research gaps are required. A systematic mapping study is utilized in giving a summary of the conducted research in a particular study area. The objective of this paper is to conduct systematic mapping studies on cloud, fog, edge/mobile devices management, hierarchy models and business models. The results showed that publications that discussed process in relations to the field of study is 14.04% out of the 114 papers included. Also method contributed 24.56%, model had 42.98% and tool contributed 18.42%. Furthermore, evaluation research in terms of the field of study was 27.5% out of 120 papers included. Also, validation was discussed in 17.5% of the papers, solution was 32.5%, philosophical was 5.83%, experience was 15.83% and opinion was 0.83%. The clearly highlighted gaps ought to inspire more enthusiasm for additional research by both researchers and industry practitioners