Digital Agriculture Needs a Broad Community of Contributors to Succeed

Here she describes some of the inherent challenges in applying data analysis to agriculture and offers some thoughts on the solution for the digital agriculture sector going forward. The digital transformation of the US economy is beginning an agricultural revolution that’s likely to revolutionize arable agriculture to a similar extent that mechanization and biotechnology did last century. There are now a growing number of startup firms working to provide producers with digital technologies to help them operate their estates, and the majority of these are doing so using a data-driven approach.

This approach involves collecting various data including data that can be collected at arbitrarily high resolution using equipment sensors, satellites, aircraft, drones, and multiple other data sources; data from sensors that can measure local conditions events, moisture level and other weighty parameters with high accuracy; and data from financial markets or seed and chemical companies. The startups are then using the mounting availability of affordable computation to process algorithms overnight on terabytes of this data.

The challenges of a data-driven approach The problem with a data-driven approach, as Dr. This makes the development, testing, validation, and successful rollout of digital technologies a lot more challenging compared to other industries where the data are usually static. As well, compared to other industries such as the consumer technology trade, access to useful data in agriculture is usually restricted by privacy concerns and corporate confidentiality, and in some cases, the data has just not been collected.

The data-driven and computer science approach taken by many digital agriculture startups has meant that the results of their efforts often do not meet expectations, since they have focused on developing sophisticated, predictive models of field harvest growth on the assumption that open field harvest expansion and health can be managed in a controlled manner, when it most certainly cannot. A systems theory approach It is clear that digital agriculture needs a new paradigm to succeed, where collaboration in between scientists and scholars from different disciplines can yield the type of results acceptable by farmers.

General models of photosynthesis and crop expansion are available from many years of research but have not been integrated into processed sensor and image data and predictive methods for individual producers. System theoretic principles can help to discard some of the sensor data and focus only on weighty inputs, thus possibly simplifying the application of machine learning in challenging scenarios with noise and other artifacts; the methods and tools from systems theory can help to choose when to react and how to react only to particular events.

By minimizing the need for data collection, the analytics engine can be used more efficiently, saving the computation time for delivering the actionable insights to the farmer in a timely manner. It is fundamentally important to account for the particularities of planting advance dynamics, analyzing the response of crops to various inputs as fertilizers, nutrients, climate changes, and so on, to try and control and manage the whole process more scientifically. The data collected by various sensors should be viewed from this perspective: namely, to use it in the best way to build more sophisticated models and be able to analyze the data in relation to the models and their evolution.

Challenges remain But there remain two challenges still: the cost of taking such an approach in a startup ecosystem where venture capital backers are bound by specific timelines; and the need to involve producers. Will data-driven artificial intelligence methods achieve a level where the data can be analyzed and learned on-the-go at a speed and cost that’s appropriate for farmers and repaid by improved planting yields? Or could a more fundamental, specific approach achieve the much-desired cost effectiveness by accounting for the harvest advance models. Helping to react only to particular events, minimizing the load of the analytics engine and the response time?

The longer road to a commercial product implied by a systems theory approach requires a substantial backing to develop the integrated models. Methods that venture capital shareholders might not be willing to bear, despite the fact that down the road, this investment could reduce the costs for the data collection, storage, analytics, and optimization. The producer has the empirical knowledge of a specific environment from first-hand observation which he or she can combine with data to decide when to plant, how to monitor expansion, when to apply fertilizers, how to manage weeds, diseases, and pests, and when to gathering.

A farmer’s motivation to cooperate in the development and deployment of digital tools. The ability to create value at the farm gate to achieve profitability, will be the key to revolutionizing farming in the era of digital industrialization.