DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy

by   Sai Gurrapu, et al.

Quantitative metrics that measure the global economy's equilibrium have strong and interdependent relationships with the agricultural supply chain and international trade flows. Sudden shocks in these processes caused by outlier events such as trade wars, pandemics, or weather can have complex effects on the global economy. In this paper, we propose a novel framework, namely: DeepAg, that employs econometrics and measures the effects of outlier events detection using Deep Learning (DL) to determine relationships between commonplace financial indices (such as the DowJones), and the production values of agricultural commodities (such as Cheese and Milk). We employed a DL technique called Long Short-Term Memory (LSTM) networks successfully to predict commodity production with high accuracy and also present five popular models (regression and boosting) as baselines to measure the effects of outlier events. The results indicate that DeepAg with outliers' considerations (using Isolation Forests) outperforms baseline models, as well as the same model without outliers detection. Outlier events make a considerable impact when predicting commodity production with respect to financial indices. Moreover, we present the implications of DeepAg on public policy, provide insights for policymakers and farmers, and for operational decisions in the agricultural ecosystem. Data are collected, models developed, and the results are recorded and presented.


Panel: Economic Policy and Governance during Pandemics using AI

The global food supply chain (starting at farms and ending with consumer...

Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning

International economics has a long history of improving our understandin...

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster prepared...

Detecting and Classifying Outliers in Big Functional Data

This paper proposes two new outlier detection methods, which are useful ...

Movement Tracks for the Automatic Detection of Fish Behavior in Videos

Global warming is predicted to profoundly impact ocean ecosystems. Fish ...

Efficient Discovery of Meaningful Outlier Relationships

We propose PODS (Predictable Outliers in Data-trendS), a method that, gi...

Supply Network Formation and Fragility

We model the production of complex goods in a large supply network. Firm...

Please sign up or login with your details

Forgot password? Click here to reset