The need, as indicated by stakeholders

To meet the food demands of the world’s growing population – 8 billion today, projected to be 10 billion in roughly 30 years – U.S. agriculture needs autonomous field machines to optimize crop growth at a detailed level (i.e., precision agriculture) to keep farming sustainable with an ever-shrinking number of farm laborers. Some autonomy has been available on farms for over 20 years; e.g., automatic guidance on tractors. However, society has not yet accepted fully autonomous vehicles, and machinery manufacturers are slow to produce them, largely due to potential liability issues, lack of clear return on investment (Erickson and Lowenberg-DeBoer, 2022), etc. Technology gaps centered on sensing and decision-making currently prevent industry from having confidence in full autonomy for multiple functionality and safety reasons. The proposed multistate research project aims to address key sensing, artificial intelligence (AI), and robotics barriers to implementing intelligent autonomous agricultural machines and enabling precision agriculture in the face of the farm labor shortage. Our vision is that small and large farms of row-crops, specialty crops, and animal production will be able to transition from large human-driven equipment to full integration of compatible AI-based autonomous machines (agricultural autonomy, or A2) that will drastically increase the capacity of the human machine operator. The research project’s outcomes will pave the way toward the transition to teams of smaller, fully autonomous, machines, both on the ground and in the air. These machines could conduct most aspects of crop production, including scouting, planting, fertilization, pest (weeds, insects, diseases) control, and harvesting, at high spatial resolution, informed by autonomously acquired data and AI-based decision tools.  We believe this transition will also benefit post-harvest processing and even agricultural research. The ultimate outcomes of the project will be (a) more prolific, profitable, and sustainable farming methods to meet world food and fiber demands; (b) safer and less labor-intensive processing systems based on agricultural-autonomy for grading, sorting, cutting, etc.; and (c) new autonomous research machines and systems that enable accurate field and greenhouse data to be collected with minimal human involvement and also enable the measurement of properties not previously measurable.

While A2 will likely ultimately eliminate the need for some machine operators, the aim is to keep farmers and farm workers in the loop and to assist them in enhancing farm profitability and sustainability.  Moreover, farm managers’ expert knowledge should be captured and incorporated into machine learning to improve decision-making and operations. The average age of the U.S. farmer is approaching 60 years, so the time is critical to capture and maintain industry expertise before it vanishes. The farm tasks to be conducted autonomously will require large volumes of data (e.g., on insects in the field, weeds, diseases) collected by various platforms including ground-based and aerial robots and possibly stationary sensor networks. All these data must be analyzed and recommendations relayed to farm managers in an understandable and trustworthy way for strategic decisions. In later stages of development, the data must be analyzed for real-time tactical decisions by autonomous vehicles, which can be terrestrial and/or aerial, working together as a team to undertake the prescribed tasks in an efficient manner. All this sensing and analysis must be accomplished in rural and remote farm fields where broadband wireless infrastructure will be limited or non-existent for the foreseeable future.

The importance of the work, and what the consequences are if it is not done

While the American people make up just over 4% of the world’s population (U.S. Census Bureau, 2020), American row-crop farms produce about 33% of the world’s soybeans, 30% of corn, 15% of cotton, 8% of sorghum, and 8% of wheat (Our World in Data, 2022). The U.S. exports tremendous amounts of these commodities to feed and clothe the world, and the global population is expected to increase by roughly 25% in the next three decades (United Nations, 2022). Furthermore, rising living standards worldwide raise the demand for animal protein, which adds an additional requirement for grains as feed. On top of these worldwide demand pressures, expectations for environmental risk mitigation and sustainability are increasing, requiring that crop inputs be reduced. Moreover, a changing climate adds uncertainty to future yield capabilities, and high-quality farmland is being lost to urbanization and road construction. The farm labor shortage is an exacerbating factor. Aside from the aging of farmers, rising living standards are reducing the desirability of farm work among the world’s young; the average age of immigrant farmworkers in the U.S. rose by 7 years between 2006 and 2021 (USDA Economic Research Service, 2023).  Moreover, immigration issues worldwide are reducing the flow of migrant farmworkers. A2 is the solution to the farm labor shortage and aging.

Autonomy is also an enabler of precision agriculture (PA), which holds great promise to help address the demand challenges.  A recent study on potato production found that PA increased economic profitability by 21% and “social profit,” a term used as an overall measure of sustainability, by 26% (van Evert et al., 2017).  PA optimizes inputs such as seed, fertilizer, irrigation, herbicide, and pesticide on individual zones in a farm field or even individual plants, thus increasing per-acre yield and using inputs more efficiently. The finer the scale, the more optimization is possible. Sensors, analytical tools, and electromechanical devices are used to make and implement these zone-specific optimization decisions. Mechanization has drastically improved the efficiency of individual farmworkers, but there is no room to further increase the size of tractors and harvesters, which have become huge, expensive, and heavy to the point of damaging crop performance by compacting the soil. Therefore, the principal remaining tool to solve the issues of labor and finer precision in PA is autonomy. PA can benefit from autonomy by having sensors on autonomous vehicles and platforms collecting farm data, computing devices determining whether to apply a specific input at a particular location, and electromechanical actuators converting the decisions into action. With the amazing advances that have been made in AI and robotics in the last few years, A2 now appears as a solution to optimizing production by increasing yield and reducing environmental risk at an extremely high level of precision, potentially even at the single-plant level, while counteracting the downward trend in available farm labor.

A long-term major collaborative effort is required to develop the multifaceted AI and capabilities needed to enable the U.S. to lead in A2.

The technical feasibility of the research

Compared to other autonomy efforts, the level of complexity in agriculture is very high. Farm fields require navigation over large areas with no established roads or signs, varying and challenging terrain, many types of obstacles, and rapidly changing surroundings like soil conditions, plant sizes, etc. The operating environment for autonomous agricultural machines will be particularly harsh, with wide variations in temperature, solar radiation, precipitation, humidity, and dust and dirt accumulation. The number and variety of objects and conditions that must be classified and quantified is large. For example, autonomous machines may be required to quantify the level of plant health; differentiate between weeds and crop plants and between various species of weeds; detect, identify, and quantify diseases and insects; and differentiate among soil conditions in terms of both fertility and texture. The ability of autonomous machines to communicate, particularly in a data-heavy environment as will be the case when imaging becomes commonplace, will be challenged by the lack of broadband connectivity in most rural and remote farm fields. Thus, edge computing will be a major component of A2. As opposed to on-road autonomous vehicles, for which the decisions are basically limited to speed and turning angle, the decisions required of autonomous agricultural vehicles will include navigation as well as numerous actions involving applying assorted farm inputs at various locations and in various amounts.

All this being said, the advancement of sensing devices, computational hardware and software, motors and mechatronics, IoT, wireless communications, etc., has recently enabled rapid development of autonomous systems, even for applications like agriculture.

The advantages for doing the work as a multistate effort

The multistate team is expected to be composed of investigators from institutions with a strong record of research and innovation in important elements of A2. Mississippi State University, University of Florida, Penn State University, Washington State University, University of California-Davis, etc., all have viable programs in autonomous systems for agriculture as well as strong faculty cohorts who are experts in agronomic, socioeconomic, and environmental aspects of production agriculture.

Different regions have different climates, crops, cropping systems, etc.  Within regions a multistate research project can bring about more rapid progress due to collaboration and idea sharing on similar problems.  Among regions, novel ideas from one application can spur innovation in other regions where novel techniques may be applied in different ways or on different crops.

What the likely impacts will be from successfully completing the work

Outcomes of this research will be advanced farming systems that enable highly optimized food production for food security and environmental sustainability. Socioeconomic studies will produce knowledge and recommendations about how A2 affects various farm scales. These outcomes will increase U.S. economic competitiveness by improving worker productivity.