7 Factors That Could Make or Break Digital Farming

Editor’s note: This article originally published in April 2018.

Digital farming is the new technology genie – a genie that is now out of the bottle and cannot be put back. It is the overarching concept that embraces the precision afforded by global positioning, integrates the resolution and fidelity of new sensors and controls, and unleashes the economics, computer horsepower, and storage capabilities of the digital revolution — the now-emergent fourth Industrial Revolution, an Agricultural Revolution. Digital farming is the digital modeling of the entire cultivation – i.e., “a digital twin,” a digital replica of physical assets, processes, and systems. And its integration with a farm management system in a digital ecosystem enables all stakeholders to investigate alternatives and generate decision-quality information when it is most critical, before the need arises.

This digital genie is out of the bottle, but before the genie can really work its magic, there are some core elements of the digital and agricultural ecosystems that must be developed and integrated to a whole system. Digital farming must automate data ingestion and analysis, and it must scale to provide the value creation that is promised. To do this, there are still some gaps that must be filled. Here are seven core concepts of digital farming and why they are important in the agricultural and digital world – the agricultural digital ecosystem.

1. An Integrating digital platform. Needed first and foremost in this digital ecosystem is an integrating digital platform. We are beginning to see what this means with the emergence and dominance of platforms like Amazon for online purchasing. Before there can be anything similar to online purchasing in agriculture, there must be a digital platform that enables and protects stakeholder access and information; automates the development and analysis of massive bodies of data; and develops, reveals, and manages the potential costs – and revenues – of these decisions.

2. A sequence of actions. Second, as with every process, there needs to be an order of precedence, a sequence of actions. Simultaneous with the advent of new digital ecosystems has been an explosion of new technologies and insights across the cultivation cycle. These technologies and learnings need to be organized, structured, modeled, and scheduled in order for their decision insights to be practically applied in the field. Many of these technologies currently exist outside of the digital ecosystem, and they need to be digitally modeled and integrated to improve decision processes and create stakeholder value.

To help make this happen, each technology can be viewed relative to its contribution to the decision process. For example, imaging must be digitally curated, analyzed, and integrated with other already-known information. In the digital environment, image analysis must be automated because human analysis is inconsistent, is not reproducible, and is always “after the event.” Once imaging is combined with already known information, like a previous presence of weeds, it can provide an indication of when, where, and why plant stress might again emerge. In this case the application of imaging might follow a sequential process that includes steps such as Indication, Detection, Identification, Localization, Decision, Mitigation, and Validation. Because we know what to look for, we can select the right sensor and the right time. And once the types of stresses and their locations are known, mitigation can be accomplished with precision and economy. Known stresses will emerge again, and their emergence will be anticipated.

3. Automation of analytics. Third, a key use of the digital environment is the automation of analytics. Automation manages the analytic processes applied to every file and every data element of that file. It analyzes this data to support some anticipated event and its required decision. This data exists in very large files, and these files must be transmitted across available communications systems. These files must be in forms that are both accessible and actionable, able to be stored and analyzed in a timely, as-needed, and cost-effective way.

This is an important piece of the puzzle. Stakeholders should not be forced to learn how to manipulate this data; instead they should have the ability to progress from data scheduling, presentation, and description to developing predictions, diagnostics, and prescriptions for action. The digital platform applies the analytics and adds knowledge to the process; it must traverse the data from what happened, to why it happened, and how it can be mitigated.

4. Soil as the productive asset. Fourth, soil is the productive asset. There needs to be a digital soil model in this ecosystem – i.e., again a digital twin, which models the soil and its productive capability. Soil is the common denominator, from one cultivation to the next. This digital twin describes and analyzes all aspects of the soil that will have an impact on cultivation. The digital modeling of the soil starts with factors such as topography, soil horizons, and soil content. This modeling includes identifying buried appliances, soil treatments, and crop protections. These all need to be geo-located; ranked sequentially from least variable to most variable; and fully described, modeled, and made digitally accessible to the analysis efforts in this ecosystem.

5. Digital description of the seed or crop. Fifth, the seed or the crop to be grown needs to be digitally described and a body of growth metrics developed that characterize the cultivation of that crop and variety. In this case, this step by itself should be a sufficient and persuasive justification for the development of digital farming. When seed production – or similar historical – information is maintained for any crop in a digital ecosystem, a wealth of data and metrics is available for the next cultivation. This would include crop and variety information like first emergence, timing of vegetative states, root growth, nutrient and moisture utilization, and fruit size and maturation. These metrics can be statistically described, and these results can be used to digitally predict and schedule when certain events in the next cultivation should be happening.

6. Weather prediction. Sixth, weather prediction already is significantly better than it was even a decade ago, and today – even as weather is happening – remote monitoring and measurement have improved significantly. This means that it is now possible to correlate weather in terms of sunlight, rain, temperature, and other variables in the cultivation. Further, it is now possible to provide real-world models that integrate the soil, seed, and weather models to provide continuous monitoring, prediction, and scheduling. This coupling of models can provide additional benefits to determine crop utilization of moisture throughout the cultivation and nutrient leaching and depletion, and provide a view of other processes that are dependent upon water as the medium for transport.

7. Stresses are persistent. Last, stresses that impact the crop are persistent. In general, if there was a weed present in the last cultivation, there probably will be a weed in the next cultivation. The location of this weed stress is known and can be geo-located within inches. This geo-location and persistence applies not only to weeds but to fungi, insects, and biologicals. Early indications of these stresses are locked in the memory of the soil, and the digital twin will remember exactly where. These stresses can be identified and – like crop metrics – these stress metrics can be monitored for when and where to look to detect the next emergence. Once these stresses are indicated and detected, further resources can be applied to identification, localization, mitigation, and validation.

These seven overarching concepts are just some of the core elements, and these need to be integrated into a whole that provides all stakeholders with the required decision-quality information when it is needed. These overarching concepts need to be pushed upstream and downstream of on-farm production to provide for cultivation and information provenance as well as agricultural stewardship.

Finally, digital farming must be economical and scalable, and the platform must create and deliver value for all stakeholders. Economics, scalability, and value creation ensure that the platform and its presented capability will be used and profitably integrated. The genie is out of the bottle, and these concepts are a part of digital farming and are leading an agricultural revolution.