Underlying Factors That Worsened the Vulnerability of Small-Holder Farmers During the COVID-19 Pandemic: Evidence from North-Central, Nigeria

This chapter determines the underlying factors that worsened the vulnerability of small-holder farmers during the COVID-19 pandemic in North-Central, Nigeria. Proffering lasting solutions to the problems of low productivity, arising from the vulnerability to cope with shock events, is an important starting point to understanding how, why, and what occurred over the last 2 years. To better understand this, the socioeconomic characteristics of small-holder farmers were identified and interlinked with the factors that impaired their vulnerability during the pandemic. Primary data were collected with the aid of a structured questionnaire in combination with face-to-face interviews. A multi-stage sampling technique was used for data collection and a total of 360 farmers were sampled across three states in North-Central. The analytical techniques employed in the study include descriptive statistics and multiple regression analysis. The findings of the research reveal that the majority of the respondents are male, fairly advanced in age, with relatively low levels of formal education. The major factors that significantly worsened the vulnerability of the farmers is educational level, farming experience, access to finance, adoption of improved technologies, and access to extension services. It is recommended that efforts should be made towards encouraging farmers to acquire some level of formal education, while adult education should be encouraged among those that are fairly old. Government should also increase access to finance and credit facilities, i.e., finance that goes directly to the actual small-holder farmer. More access to various improved farming technologies should provide farmers with better crop output and financial turnover. Moreover, increased access to quality extension services should be made available. In brief, the COVID-19 pandemic created financial uncertainties which affected economic growth and investments throughout different sectors of the world economy. It created agricultural setbacks that should not be overlooked but rather documented to mitigate future shocks with preparedness as a new standard. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Brief Review of Convolutional Neural Network Techniques for Masked Face Recognition

Masked face recognition is now an essential part of health safety, security and surveillance systems which offers incredible advantages in our daily lives, especially in the era of the pandemic ushered in by the outbreak of coronavirus disease in the year 2019 (COVID-19). Applications such as;face mask compliance checks, facial security checks, facial attendance records and facial authentication for access control now requires an effective masked face recognition system. The existing systems of masked face recognition were developed to automatically detect and understand faces occluded with masks using computer vision and deep learning techniques, the systems are yet to work effectively in real-time. This study gives an analysis of some techniques used for the implementation of masked face recognition system, with emphasis on Convolutional Neural Network (CNN). The strengths and enhancement areas of the highlighted techniques towards real-time implementation were discussed. © 2021 IEEE.