precision agriculture

Precision Agriculture Series: AgriTech and the Data Challenges of 2021

This is the first part of our “Precision Agriculture series.” In this part, we’ll introduce precision agriculture and how precision agriculture is driving value in the market. We’ll also discuss how every successful AI project relies on the data- both its quantity and quality. Be sure to stay tuned for our second post addressing the first challenge in precision agriculture: Workforce Management.

If we assess the landscape today in the agriculture industry we are facing some pretty strong facts. To start, the world population is expected to reach about 9 billion by 2050, and yet there is only 4% of the earth’s surface available for farming, and maximizing food production is vital for us all. But the good news is farmers and agronomists can leverage new AI-driven solutions in order to improve crop yields and drive business value. 

If we throw another curveball into the mix this past year, we’ve got another challenge, Covid-19. Covid-19 has further exposed us to the vulnerable agricultural landscape and made us question the sustainability of global food demands, and with all these unfavorable factors against us- it puts us at further risk. 

What is the answer? It lies in achieving efficiency: generating more with less, now more than ever before. But this isn’t the only challenge that exists in the agritech industry. That brings us to the topic of data labeling. When it comes to data labeling, we have highlighted five primary issues handicapping its efficiency.

In this series, I will focus on one challenge per blog post and drill down into each challenge. In my last post of the series, I will provide you with a solution to these challenges. So, in order to get started, let’s dive into the basics. 

What is Precision Agriculture?

Precision agriculture is a farming management system based on the use of modern technologies at every stage of work. Usually, a field has heterogeneous zones, and technologies allow to identify zones and manage this variability. As a result, farmers use seeds, fertilizers, and pesticides more efficiently; this also helps to increase the harvest. When it comes to farming it is important to remember the role the farmer plays in the equation. Farming is a very visual task, it’s about using your eyes and thinking how a farmer would assess the situation based on his experience and based on that, assess what they should do. This is where data plays a key role and technology can step in and navigate the decisions at hand, based on the insights the data provides. 

How Precision Agriculture Is Driving Value 

Machine learning (ML)  modeling, the use of drones and robots, and integrated data analysis are already having a noticeable impact on the farming sector. Combining AI in farm equipment and operations has produced a 30% drop in expenditure on farming in a number of countries. As available farm labor dries up worldwide, AI-driven bots are assisting in the process of harvesting crops faster and with greater care. Overall providing better productivity outputs. 

Mask conversion annotation with the Dataloop platform

Weeds cost farmers an estimated $43 billion in lost crops each year. But a combination of more efficient robot weeders and drones allows farmers to:

  • Apply chemicals with greater precision
  • Collect data analysis which provides early alerts about the appearance of invasive weeds
  • Helps farmers keep weeds under control while using 20x fewer herbicides.

Precision agriculture is one of the leading ways that tech is helping improve nutrition for everyone. Drones are equipped with high-resolution cameras and hyperspectral, multispectral, and/or thermal sensors, and static IoT devices are strategically placed across every farm, delivering masses of valuable unstructured data. Through ML and accurate data, precision agriculture enables farmers to monitor crop growth, soil condition, weather patterns, and more, increasing yields, lowering costs, and serving as the first step towards autonomous crop management. 

Precision agriculture lets farmers know when to use fertilizers, pesticides, and herbicides, how much is needed, and where and when you can get the best results.  This assures farmers are getting the earliest warning signs to protect their crops. 

This also helps cut costs for farmers, improves the health and quality of the crops, and reduces the number of chemicals released into the environment. Cattle facial recognition technology and livestock monitoring enables a similar revolution for livestock and dairy farming, allowing farmers to track individual animals, spot the earliest signs of ill-health, and monitor feeding patterns. 

Livestock monitoring data management with Dataloop

But Lack of Data Still Undermines AI Success

This is where we encounter a problem. Many agritech companies are still a long way away from tapping into the full power of AI, and its data that causes them to fall short. 

Of all the resources that agro-technicians draw on, data is the most important. 

At Dataloop, we’ve frequently accompanied farmers through their digital transformation and helped them apply AI and ML to their farming business. Time and time again, we’ve seen farmers stumble at the challenge of efficient data labeling and data management at scale. 

Time and time again we’ve seen farmers stumble at the challenge of efficient data labeling and data management at scale.  Click To Tweet

Every successful AI project relies on the data that feeds it, and that’s just as true for the farming community. Farmers need data that is accurately labeled, consistent, and trustworthy so that they can establish reliable models to identify the patterns that data scientists depend upon for every AI project. 

There’s no lack of data in any agriculture business, but just like produce, it doesn’t arrive washed, graded, and ready to serve.   Click To Tweet

Farmers are constantly flooded with raw data. High-resolution images from drones and static cameras, data points from sensors, and information from other equipment arrive every minute, swamping agriculture technicians with varied, inconsistent, unstructured data that’s not usable, as-is. Converting raw data into labeled data requires the application of complex ontologies, attributes, and different annotation types to the ever-growing heap of data, and to do so accurately, quickly, and at a reasonable price tag is a common challenge. 

It’s no surprise that 19% of businesses blame lack of data and data quality issues as the biggest obstacles preventing them from fully adopting AI. A whopping 80% of AI project time goes on processing and labeling data for deep learning (DL) and ML models.

In our own experience, we’ve repeatedly seen AI projects fail due to poor data labeling. Quality data at scale is the keystone for success for any AI and ML project; when you improve the quality and/or accuracy of your datasets, you’ll transform your entire project. 

With data labeling playing such a vital role in any AI development for agriculture, we examined the underlying issues that prevent farming enterprises from generating trustworthy labeled data at scale. Through this we discovered that there are five primary issues handicapping efficient data labeling:

  1. Workforce management
  2. Dataset quality
  3. Financial obstacles
  4. Data privacy
  5. Smart tooling

Fully understanding the source of data labeling challenges will allow us to strategize ways to overcome them and increase success rates for AI agriculture projects. 

In our next post, we will dive into the first challenge of data labeling: Workforce Management. In the meantime, if you’d like to learn more about Dataloop’s precision agriculture solution, click here.

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