SMART AGRICULTURE BEYOND INDUSTRY: ANALYZING THE FUTURE OF AGRICULTURE THROUGH SOCIAL MEDIA INSIGHTS

https://scholar.dsu.edu/research-symposium/1011/thumbnail.jp

USING SOCIAL MEDIA FOR CUSTOMER KNOWLEDGE MANAGEMENT IN DEVELOPING ECONOMIES: A SYSTEMATIC REVIEW

Knowledge Management (KM) research has theorized that KM activities lead to better firm performance. The current view that there is more external knowledge than exists within organizations has resulted in a preference for customercentric approaches over traditional KM activities. Recent advances in technology has resulted in social media use as an avenue for knowledge creation from the social interaction between brands and their customers. Most research however explore social media’s impact on organizational knowledge in developed economies with little attention to developing economies where KM could be conceptualized differently. In this research, we analyze the extent to which social media can support customer knowledge management (CKM) in developing economies. Using a systematic literature review, the current study captures literature containing keywords on social media and CKM published in relevant databases between January 2010 and December 2019. Finding from this study demonstrated that the strategies employed in developing economies largely depend on the type of social media and strategies can be made more effective through channel, engagement, and business intelligence management. It was also discovered that knowledge creation was the most important KM process for ensuring success while the systems that support this were a combination of Marketing, Sales, Customer Service, and Technology

The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis

Artificial intelligence (AI) has emerged as the defining technology of the 21st century and has far-reaching impacts on project management (PM). This study assesses the applications of AI in managing project processes and targets through a systematic analysis of publications from 2017 to 2021. The analysis has revealed interesting insights, trends, gaps, and issues. This study informs the researchers and practitioners of the status of AI applications in the management of project processes and targets. It helps stimulate research efforts that can lead to more advances in applying AI to augment PM practices

Agile Project Management: A Systematic Literature Review of Adoption Drivers and Critical Success Factors

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. Nine (9) drivers of adoption and thirteen (13) critical success factors related to the project dimensions of Project, Team, and Culture. The findings of this study outline the current state of APM adoption and use and is relevant to project management practitioners and researchers

A Deep Learning Model Compression and Ensemble Approach for Weed Detection

Site-specific weed management is an important practice in precision agriculture. Current advances in artificial intelligence have resulted in the use of large deep convolutional neural networks for weed detection. In this paper, a transfer learning, model compression, and ensemble learning approach is introduced that is suitable for resource-limited hardware such as mobile and embedded devices. The resulting ensemble model achieves 91.2% classification accuracy which is comparable to the performance of state-of-the-art deep learning models (such as the vanilla VGG16, DenseNet, and ResNet) while being about 62.22% smaller in size than DenseNet (the smallest-sized full-sized model). The approach used in this study is beneficial for further development of deep convolutional neural networks on smaller resource-limited hardware typically used in agriculture, as well as other industries such as healthcare and telecommunication

Early Public Outlook on the Coronavirus Disease (COVID-19): A Social Media Study

The recent outbreak of the coronavirus (COVID-19) brought with its public concerns and fears about a global epidemic. With the increase in the popularity, usage, and reach of social media, this research examined the early public outlook on COVID-19 using SM-Platform, Twitter.com. The current study employed a mixed-method approach in collecting and analyzing public tweets by combining quantitative sentiment analysis with a qualitative thematic analysis. Our results revealed positive sentiment prior to the spread of the disease. The sentiment then turned negative as the disease spread, accompanied by a large amount of fear as rumors. In a thematic analysis we also uncovered nine key topics on the disease including, but not limited to, prevention, symptoms and spread of disease. Our study will provide an understanding of social media and public health outbreak surveillance. The findings of the research revealed the usefulness of twitter mining to illuminate public health education

Transfer-Learned Pruned Deep Convolutional Neural Networks for Efficient Plant Classification in Resource-Constrained Environments

Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control 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. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial intelligence, 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. Consequently, although DCNNs have shown continuous accuracy improvements in research settings, they remain relatively unused for practical purposes in precision agriculture due to their large number of parameters and the difficulty to implement on resource-constrained devices. Accordingly, this research investigated the use of model compression to reduce complexity and increase the efficiency of DCNNs in low-resource conditions. The proposed approach involves stacking two pre-trained DCNN models – Xception and DenseNet – to reduce the effect of performance degradation during the model compression process. A performance evaluation of the resulting XD-Ensemble indicated that the model outperformed both state-of-the-art DCNNs and a lightweight EfficientNet-B1 model in a resource-constrained environment in terms of prediction accuracy, model size, and inference speed. The current study contributes to enhancing viability while minimizing the environmental footprint of agricultural technologies as well as maximizing their production efficiency