The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in this field would find the discussions in this chapter insightful.
PurposeDespite the sporadic evolution of artificial intelligence, the most valuable asset of any organization in the modern world is human resources. This study aims to reveal that partnerships between higher education institutions (HEIs) and employers will ease the process of employee mid-career development in Uganda's corporate employment sector by promoting work-based postgraduate training, and this additionally promotes human resources (HR) capacity-building for organizations.Design/methodology/approachThe hypothesis is that contemporary employees seek out an academic mid-career development postgraduate programme that is blended to fit into the employees' work schedule. The study was a descriptive quantitative study, and a closed-ended questionnaire was sent out to groups of corporate employees online (N = 70) and 41 responded, giving a response rate of 58.5%.FindingsFindings indicate a need for a flexible program for mid-career development and transition, the low standard deviation of (Neutral = 0.95, Disagreed = 2.64 and Agreed = 3.3) implies an insignificant deviation from the mean of responses. Indeed, over 95% agree that pursue further studies is needed but in a more flexible way.Research limitations/implicationsThe study design was limited by the sample selection process and study design. In the future, the authors recommend a mixed study for both quantitative and qualitative dimensions of such studies.Practical implicationsIrrespective of gender, hierarchy and experience, employees want flexible study modes for their postgraduate. This implies that institutions of higher learning should work with the labour industry and position themselves as work-based information and communication technology (ICT)-Integrated learning theatres.Originality/valueThe move towards a collaborative strategy between academia and the employment industry is very evident in this study.
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