No abstract

No abstract

With an emphasis on adaptive processes that respond to uncertainties, the Agile Project Management (APM) approach has evolved the way projects are managed beyond the traditional processes. This study aims to investigate recent literature on APM to discover the adoption drivers and the critical success factors that influence APM success and provide recommendations for the development of APM best practices. The study conducted a literature search on academic databases ABI/Inform, ACM Digital Library, EBSCO Host, and IEEE Xplore with keywords 'agile' and 'project management' for peer-reviewed English language articles published between January 2015 and January 2020 to discover insights regarding adoption drivers and critical success factors. Eleven (11) drivers of adoption and thirteen (13) critical success factors related to the project dimensions of Project, Team, and Culture were discovered. The findings of this study outline the current state of APM adoption and use and is relevant to project management practitioners and researchers.

No abstract

To prevent yield losses, it is critical to eliminate competition between food crops and weeds at the onset of plant growth. While uniform spraying of herbicides can be economically and environmentally inefficient, site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. Past research on weed detection in SSWM has used a large deep convolutional neural network (DCNN) for weed detection. These models are, however, computationally expensive and prone to overfitting on smaller datasets. In this paper, we propose an approach to detecting weeds amongst plant seedlings using transfer learning in a small network. Our approach combines the mobilesized EfficientNet with transfer learning to achieve up to 95.44% classification accuracy on plant seedlings. Due to the robustness of transfer learning methods, this approach would be beneficial in improving both the classification accuracy and generalizability of current weed detection methods.