92 research outputs found
An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction
Accurate prediction of agricultural crop prices is a crucial input for
decision-making by various stakeholders in agriculture: farmers, consumers,
retailers, wholesalers, and the Government. These decisions have significant
implications including, most importantly, the economic well-being of the
farmers. In this paper, our objective is to accurately predict crop prices
using historical price information, climate conditions, soil type, location,
and other key determinants of crop prices. This is a technically challenging
problem, which has been attempted before. In this paper, we propose an
innovative deep learning based approach to achieve increased accuracy in price
prediction. The proposed approach uses graph neural networks (GNNs) in
conjunction with a standard convolutional neural network (CNN) model to exploit
geospatial dependencies in prices. Our approach works well with noisy legacy
data and produces a performance that is at least 20% better than the results
available in the literature. We are able to predict prices up to 30 days ahead.
We choose two vegetables, potato (stable price behavior) and tomato (volatile
price behavior) and work with noisy public data available from Indian
agricultural markets.Comment: 9 pages, 3 figures, 3 table
Big Tech corporations and AI: A Social License to Operate and Multi-Stakeholder Partnerships in the Digital Age
The pervasiveness of AI-empowered technologies across multiple sectors has led to drastic changes concerning traditional social practices and how we relate to one another. Moreover, market-driven Big Tech corporations are now entering public domains, and concerns have been raised that they may even influence public agenda and research. Therefore, this chapter focuses on assessing and evaluating what kind of business model is desirable to incentivise the AI for Social Good (AI4SG) factors. In particular, the chapter explores the implications of this discourse for SDG #17 (global partnership) and how this goal may encourage Big Tech corporations to strengthen multi-stakeholder partnerships that promote effective public-private and civil society partnerships and the meaningful co-presence of non-market and market values. In doing so, the chapter proposes an analysis of the sociological notion of "social license to operate" (SLO) elaborated in the mining and extractive industry literature and introduces it into the discourse on sustainable digital business models and responsible management of risks in the digital age. This serves to explore how such a social license can be adopted as a practice by digital business models to foster trust, collaboration and coordination among different actors - AI researchers and initiatives, institutions and civil society at large - for the support of SDGs interrelated targets and goals
Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Big streams of Earth images from satellites or other platforms (e.g., drones
and mobile phones) are becoming increasingly available at low or no cost and
with enhanced spatial and temporal resolution. This thesis recognizes the
unprecedented opportunities offered by the high quality and open access Earth
observation data of our times and introduces novel machine learning and big
data methods to properly exploit them towards developing applications for
sustainable and resilient agriculture. The thesis addresses three distinct
thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP),
the monitoring of food security and applications for smart and resilient
agriculture. The methodological innovations of the developments related to the
three thematic areas address the following issues: i) the processing of big
Earth Observation (EO) data, ii) the scarcity of annotated data for machine
learning model training and iii) the gap between machine learning outputs and
actionable advice.
This thesis demonstrated how big data technologies such as data cubes,
distributed learning, linked open data and semantic enrichment can be used to
exploit the data deluge and extract knowledge to address real user needs.
Furthermore, this thesis argues for the importance of semi-supervised and
unsupervised machine learning models that circumvent the ever-present challenge
of scarce annotations and thus allow for model generalization in space and
time. Specifically, it is shown how merely few ground truth data are needed to
generate high quality crop type maps and crop phenology estimations. Finally,
this thesis argues there is considerable distance in value between model
inferences and decision making in real-world scenarios and thereby showcases
the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi
Machine Intelligence in Africa: a survey
In the last 5 years, the availability of large audio datasets in African
countries has opened unlimited opportunities to build machine intelligence (MI)
technologies that are closer to the people and speak, learn, understand, and do
businesses in local languages, including for those who cannot read and write.
Unfortunately, these audio datasets are not fully exploited by current MI
tools, leaving several Africans out of MI business opportunities. Additionally,
many state-of-the-art MI models are not culture-aware, and the ethics of their
adoption indexes are questionable. The lack thereof is a major drawback in many
applications in Africa. This paper summarizes recent developments in machine
intelligence in Africa from a multi-layer multiscale and culture-aware ethics
perspective, showcasing MI use cases in 54 African countries through 400
articles on MI research, industry, government actions, as well as uses in art,
music, the informal economy, and small businesses in Africa. The survey also
opens discussions on the reliability of MI rankings and indexes in the African
continent as well as algorithmic definitions of unclear terms used in MI.Comment: Accepted and to be presented at DSAI 202
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