With an expected compound annual growth rate (CAGR) of 16.9% to reach a whopping $7.16 billion by 2028, the global smart crop monitoring market appears to be on a roll with no signs of slowing down.
“This growth of the crop monitoring market is likely to be driven by the increasing demand for optimized crop yields and efficient use of resources in agriculture,” said Research and Markets in its recent report. “Additionally, advancements in technologies such as AI, machine learning, and remote sensing are expected to further propel crop monitoring growth.”
The research firm says drivers behind this momentum include a combo of “shrinking availability of agricultural land due to rapid urbanization” plus the “burgeoning global population” — and that the use of smart crop monitoring technologies that leverage precision agriculture and the internet of things (IoT) helps to optimize resource usage.
“This efficiency is crucial for meeting the increasing food demands of a growing population while conserving valuable agricultural resources and minimizing environmental impact, making crop monitoring an essential tool in modern agriculture,” according to the firm.
Part of the broader category of precision agriculture — aka precision farming — smart crop monitoring integrates various technologies to improve efficiency, optimize crop yields, and boost the bottom line for farmers who implement them.
Here, we take a brief look at some of the most popular technologies currently in use; benefits and challenges of adoption; and what a recent GAO report says about how the federal government can help.
Popular agri-tech tools
Agri-tech, also referred to as agtech, enables precision agriculture through the use of technologies that are also popular in other industries — such as sensors, robotics, satellites, and drones — underpinned by data analytics to extract value from the massive volume of data accumulated.
According to The Farming Insider, some of the most popular agri-tech tools in use today include:
- Remote Sensing: “By using satellite imagery or drones equipped with imaging sensors, remote sensing allows farmers to assess the health of crops across large areas,” providing a “more comprehensive and timely overview” to help farmers spot potential problems early so they can intervene appropriately.
- Internet of Things (IoT): “IoT technology enables the collection of real-time data through various sensors placed in the field, including soil moisture sensors, temperature sensors, and humidity sensors,” which enables data-driven decision making about various crop inputs.
- Machine Learning: “With the help of machine learning algorithms, data collected from various sources…can be analyzed to detect patterns and predict crop health,” which reduces the need for “subjective interpretations” and makes crop monitoring more accurate.
- Mobile Applications: “Agri-tech also offers mobile applications that enable farmers to access real-time information about their crops,” along with “personalized recommendations, alerts, and actionable insights” in support of prompt and informed decision making.
Challenges and benefits
In a post for Thomasnet.com®, writer Chad Brinkle also describes the various agricultural technologies enjoying growing popularity and underscores the need for farmers to receive adequate training to make the most of them.
“Precision agriculture requires farmers to undergo specialized training to effectively utilize these technologies and interpret the data they generate, highlighting the importance of education and skill development in modern farming practices,” Brinkle writes, noting that limited access to training could create a challenge for effective adoption.
Other challenges he cites include:
- High costs: “…high initial costs and equipment availability associated with adopting advanced technologies such as GPS, sensors for soil management, and drones” can be a significant barrier for “small family farms.”
- Data security: “…concerns about data security arise due to the sensitive nature of the information collected and shared within these modern agriculture systems.”
Despite security concerns about data, the real-time insights it provides enables the data-driven decision making that can be the make-or-break difference for farmers. With so many factors beyond their control, the ability to act based on objective data — rather than subjective experience or instinct — can help to optimize operations.
“Predictive modeling techniques and site-specific crop management allow farmers to forecast yields, identify potential risks, and plan accordingly, mitigating losses and maximizing profitability,” Brinkle says. “Additionally, farm management software provides tools for organizing, visualizing, and interpreting data, enabling farmers to make informed choices about resource allocation, crop rotation, and pest management. Ultimately, data-driven decision-making empowers farmers to adapt to changing conditions, improve efficiency, and achieve better outcomes in agriculture.”
Precision agriculture: GAO report
At the end of January 2024, the U.S. Government Accountability Office (GAO) issued a new report: Precision Agriculture: Benefits and Challenges for Technology Adoption and Use. The report is the result of provisions in The Advancing IoT for Precision Agriculture Act of 2021 for GAO to conduct a technology assessment and review federal programs.
The report examines:
- Emerging precision agriculture technologies and precision agriculture technology adoption
- Federal programs providing support for precision agriculture
- Benefits and challenges of adopting and using precision agriculture technologies
- Policy options that could address challenges or help enhance benefits of adopting and using precision agriculture technologies
Broadly, GAO found that precision agriculture technologies “can improve resource management through the precise application of inputs, such as water, fertilizer, and feed, leading to more efficient agricultural production.”
Specifically, GAO said benefits to using precision agriculture technologies include:
- Increased profits. “Farmers can increase yields and thus profits with the same amount of inputs or achieve an equivalent yield with fewer inputs.”
- Reduced application of crop inputs. “Technologies can reduce the application of crop inputs such as fertilizer, herbicide, fuel, and water. They can also address water scarcity by promoting the efficient use of water in agriculture.”
- Environmental benefits. “Technologies can prevent excessive use of chemicals and nutrients in a field, potentially reducing runoff into soil and waterways.”
GAO said challenges to broader adoption include:
- High up-front acquisition costs. “Acquisition costs for the latest technologies can be prohibitive for farmers with limited resources or access to capital.”
- Farm data sharing and ownership issues. “Concerns regarding farm data sharing and ownership can pose obstacles to the widespread use of AI in agriculture.”
- Lack of standards. “An absence of uniform standards can hamper interoperability between different precision agriculture technologies.”
To address these challenges or “help enhance benefits of adoption,” GAO examined three “policy goals and associated options,” which include:
- Encourage greater adoption:
- Provide additional incentives or other financial support
- Better understand and quantify benefits and costs
- Promotion and outreach to farmers
- Encourage further innovation:
- Conduct research and development to improve on-farm data gathering and analysis
- Promote the development and use of standards
- Manage greater amounts of data
- Enhance data analysis
- Encourage data sharing
The agency says these policy options identify “possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. In addition, for each policy goal, policymakers may choose no additional policy interventions, maintaining the status quo by continuing existing activities.”
As GAO notes, although many farmers are interested in adding agri-tech to their operations, there’s a growing concern about who owns the data being generated from all this technology — and what’s being done with it.
Please join us next week as we dig into that more— plus, we’ll take a look at several agtech companies recently making headlines.
