The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.

PurposeThe shutdown of normal face-to-face educational method of learning caused by the coronavirus disease 2019 (COVID-19) pandemic has made the education stakeholders reconsider and rethink education anew in light of the emerging challenges and opportunities imposed on e-learning in higher education in Nigeria post COVID-19. This study investigates the challenges confronting e-learning in higher education in Nigeria amid COVID-19.Design/methodology/approach Drawing upon data collected through a structured questionnaire administered to 395 lecturers in various disciplines in private, state and federal universities in Nigeria, the study adopts a quantitative research method. The quantitative data were analyzed using descriptive statistics.FindingsThe findings indicate that Nigeria Higher Education Institutions (HEIs) are still in the early stage of adopting the e-learning mode of study. In addition, there was no existing e-learning curriculum before the pandemic. Also, adopting e-learning mode of teaching was an uphill task for both the lecturers and the learners, given the lack of experience in information and communications technology usage and inadequate infrastructure to support e-learning.Research limitations/implications A major limitation of the study is the inability to investigate the challenges facing students in using e-learning tools due to the unavailability of access to students during the lockdown. However, the limitations create opportunities for further studies into the subject matter.Originality/value The study is timely given that HEIs in Nigeria and some other countries in Africa are yet to adopt blended teaching methods. Literature reveals that most HEIs in Nigeria are using only brick and mortar mode of teaching despite the benefit of blended learning in a pandemic situation.

Mobile technology has made communication easier and faster. People communicate in a matter of picoseconds, with little or no inhibition, regardless of their distance or location. Mobile networks are rapidly expanding all over the world. The demand led to the evolution of different technologies to meet with traffic challenges. Challenges are still evident as the cellular network system faces dynamic and chaotic behavior that needs to be resolved intelligently without human intervention. The current paper presents the state of the art of artificial intelligence (AI) in enhancing the performance of cellular networks. This paper summarizes the AI concept and reviews its applications in cellular network design, operations, and optimization. A special focus is laid on the advantages and disadvantages of AI application and a holistic study of the challenges is undertaken in order to give new research directions.

In cellular network activities, before a site is integrated it is expected that each cell of the site meets the Nigerian Communication Commission (NCC) standard of ≥98% for both service accessibility and call completion rate which in turn depicts a ≤2% in both blocked call rate (BCR) and dropped call rate (DCR). It is suggested that weather conditions have a very strong negative effect on the performance of wideband code division multiple access (WCDMA) network as it could lead to signal attenuation or change the polarization. In this paper, we study the impact of weather conditions on WCDMA network in Nigeria. To achieve this, network samples (log-files) were collected weekly during a driving test in Enugu State Nigeria for a period of five years for both rainy and dry seasons, in which blocked and dropped calls were extracted. Results show that during adverse weather conditions, BCR and DCR rise greater than 8% and 4% respectively. Although with a slight relationship between the weather conditions, the weather condition during the dry season has a better-blocked call rate of 8.76% in comparison with the rainy season with 12.89%. Calls tend to drop more during the dry season. From the outcome of the experiment, a model was developed for predicting an unknown network call statistics variables.

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