With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.

The prediction of crop yields internationally is a crucial objective in agricultural research. Thus, this study implements 6 regression models (Linear, Tree, Gradient Descent, Gradient Boosting, K- Nearest Neighbors, and Random Forest) to predict crop yields in 196 countries. Given 4 key training parameters, pesticides (tonnes), rainfall (mm), temperature (Celsius), and yield (hg/ha), it was found that our Random Forest Regression model achieved a determination coefficient (r^2) of 0.94, with a margin of error (ME) of .03. The models were trained and tested using the Food and Agricultural Organization of the United Nations data, along with the World Bank Climate Change Data Catalog. Furthermore, each parameter was analyzed to understand how varying factors could impact overall yield. We used unconventional models, contrary to generally used Deep Learning (DL) and Machine Learning (ML) models, combined with recently collected data to implement a unique approach in our research. Existing scholarship would benefit from understanding the most optimal model for agricultural research, specifically using the United Nations data.

Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variations. Managing resource inputs such as fertilizer and irrigation in the face of climate change, dwindling supply, and soaring prices is nothing short of a Herculean task. The ability of machine learning to efficiently interrogate complex, nonlinear, and high-dimensional datasets can revolutionize decision-making in agriculture. In this paper, we introduce a reinforcement learning (RL) environment that leverages the dynamics in the Soil and Water Assessment Tool (SWAT) and enables management practices to be assessed and evaluated on a watershed level. This drastically saves time and resources that would have been otherwise deployed during a full-growing season. We consider crop management as an optimization problem where the objective is to produce higher crop yield while minimizing the use of external farming inputs (specifically, fertilizer and irrigation amounts). The problem is naturally subject to environmental factors such as precipitation, solar radiation, temperature, and soil water content. We demonstrate the utility of our framework by developing and benchmarking various decision-making agents following management strategies informed by standard farming practices and state-of-the-art RL algorithms.

The Linux Foundation has today announced that its AgStack project will host a new open-source code base and computation engine that offers a data dataset of registry data for agricultural fields to aid in food traceability, carbon tracking, crop production and other field-level analytics. The computation engine is fully automated and creates, maintains and hosts the global dataset based on code contributed by Dr. Sherrie Wang, Dr. Francois Waldner and Professor David Lobell at The Center on Food Security and the Environment at Stanford University. Funding for the project came from various organizations, including the NASA Harvest Consortium. Said to be the first of its kind, the AgStack Asset Registry dataset was built and is continuously updated using data from satellites and actual field registrations that contain information on boundaries, not ownership. The data is then used to train machine learning models to ascertain more boundaries.

Technology is reshaping most activities humans do today. Concepts like Smart Farming have gained prominence as newer methods for crop and farm management are on the rise. It is making farming an efficient and profitable activity. Going by the estimates, there will be a 15% increase in the demand for agricultural products in the coming decade. Using tech solutions to cope up is an ideal way forward.

"AI is the new Electricity" – Andrew Ng* Since the advent of 20th century electricity became the main source of invention in every major industry ranging from transportation, manufacturing to healthcare, communications and many more. Today Artificial Intelligence (AI) is bringing the same big transformation across all the major industries. The part of AI that is rapidly growing and which is driving most of these transformations is Deep Learning. Today, Deep Learning has become one of the most sought after skills in the technology world. Agriculture is one industry where Deep Learning scientists and researchers are working with farmers to help them with their produce.