Leveraging Data Science to Tackle the Biggest Challenges in Farming

data-driven farming

If there’s one sector of the economy where technology integration has been transformative, it’s agriculture. Data science has created a new era in farming globally, owing to innovations such as algorithms, remote sensing, artificial intelligence (AI), and machine learning. Each works together to help farmers understand what the data is saying.

As a result, farmers can deal with and find solutions to challenges around critical resources like water management, soil health, and crop monitoring. Since data science has only scratched the surface of farming, there is the potential for even better outcomes. Let’s explore a few.


Table of Contents

Understanding Modern Agriculture’s Biggest Challenges

The Role of Data Science in Modern Agriculture

Benefits of Applying Data Science in Agriculture

Essential Data Science Tools & Techniques in Agriculture

Overcoming Barriers to Data Science Adoption in Agriculture



Understanding Modern Agriculture’s Biggest Challenges

Despite the ag industry’s willingness to adopt technology, growers face no shortage of challenges, including the one-two punch of climate change and resource depletion.

Resource depletion is exacerbated by climate change, which threatens to reduce crop yields between 6% and 10% by 2050. From climate change to soil erosion to learning emerging technologies as a tool, farmers must continually adapt, invest, and innovate to overcome some of the industry’s most pressing challenges.

These include but are not limited to:

  • Food Production: The global population is on track to reach 7 billion by the year 2050, a reality that will require bolstering food production by approximately 70%. This is one of the significant challenges a modern farmer faces, as there are already many people on the planet with barely enough to eat.
  • Pest Management: Pests are a significant problem in agriculture, costing the industry between 20% and 40% of global crop production each year, according to the USDA. While pesticides have managed to keep threats to food security at bay, the environmental cost has been high.
  • Water Scarcity: The ag industry is No. 1 for water consumption, representing an average of nearly three-quarters of global freshwater withdrawals. Water demand increases alongside rising demand for irrigated food production, not to mention rising demand stemming from other sectors of the economy. Improved irrigation practices can result in better yields for farmers.
  • Soil Health: Soil erosion, when soil deteriorates gradually from the impact of the elements, takes $100 million in income out of farmers’ pockets each year, according to the USDA.

The Role of Data Science in Modern Agriculture

Collecting data in agriculture is nothing new and harkens back to George Washington, who maintained statistical records on his farm and those in his community. Back in 1790, out of the 4 million Americans that were tallied as part of a census, 90% of them resided on farms, where the line between producers and consumers was blurred.

Even then, farmers worried about which crops to plant and how to increase the chances of a plentiful harvest. Washington participated in the country’s maiden agricultural survey in which the first crop report was produced analyzing land value, crops, yields, and more.

Data science in agriculture has come a long way, allowing farms and communities to harness large amounts of tech-driven data to gain insights about farming. Data can be used to manage resources better, adopt new technologies, and adjust the approach to advance farming and agriculture worldwide. The role of data science in modern agriculture has been life-changing for farmers globally, resulting in better yields, less water consumption, and higher crop quality.

Data science has a role in critical areas of agriculture, including:

  • Water Management: Water management is perhaps the most crucial aspect of agriculture where data science has emerged. Data science tools allow scientists and farmers to collect information about changing weather partners, soil health/moisture, and irrigation usage. As a result, they can better manage their water use, resulting in less waste and greater cost efficiency.
  • Crop Management: Data science has helped the ag industry produce high-tech crop monitoring systems, where using sensors and drones to collect data allows growers to identify problem areas before it’s too late.
  • Soil Health: Data science can also be applied to soil analysis, where the use of sensors allows scientists to create better models on soil health so farmers can assess their soil management needs in areas like fertilizer, irrigation, etc.
  • Crop Outlooks: Data science also has a role in crop forecasting, as data helps growers monitor yields and soil health and uncover insects and diseases, improving crop quality and resulting in better overall farming operations.

Benefits of Applying Data Science in Agriculture

Data science has improved agriculture in many ways, helping to bring the industry forward at a critical time in history, with a population boom and the long-term effects of climate change waiting in the wings.

Through data science, the ag industry can use crop inputs such as planting, fertilizers/spraying, tillage, and harvesting with greater specificity for better outcomes. Data science gives the agriculture industry a window into the best-growing conditions for various crops, helping farmers make decisions that will result in better crop yields.

Tools like machine learning and harnessing data like temperature patterns, rainfall, soil health, fertilizer, etc., can help farmers decide which crops to grow and when for the best potential yields. Predicting disease and pest outbreaks before they happen is a game-changer for growers.

Data science does this in various ways, including forming emerging plant types that are more disease and pest-resistant than others. Data breakthroughs in areas like sensors and machine learning help the experts to understand the traits of crops to uncover any pest or disease-related damage or vulnerabilities before it’s too late.

Machine learning is a type of data science that can be applied to and benefit crop predictions, a vital part of the farming process. Better yield estimations and improved crop nutrient nitrogen management are paramount to agriculture production. Technologies like remote sensing are helping in these areas, paving the way for more informed decision-making in modern technology for better crop yields while streamlining costs and the environmental impact.

The use of technology helps farmers access tools that help increase production via a combination of good timing and the right decisions. Climate change only worsens pests and disease infestations, the effects of which scientists continue to study.

Essential Data Science Tools & Techniques in Agriculture

Data science is changing how farmers make decisions and the techniques used by experts in the field, particularly over the past few years. Innovations around satellite and drone imagery, machine learning algorithms, big data, and even the Internet of Things (IoT) have expanded the ag industry’s access to data, putting the power in their hands to make more informed decisions.

Satellites and drones assist with the collection of data from the field. Drones capture images and study crops, analyzing data to determine where changes are needed, either offensively (to bolster yields) or defensively (to protect against potential vulnerabilities). Satellites offer a more comprehensive perspective, surveilling acreage and providing a more holistic view of crop performance. Drones are generally less expensive than satellites, but the technology is also more challenging to scale.

The combination of data algorithms and machine learning packs a powerful punch, delivering data analysis that can be directed toward predicting crop performance. Farmers effectively use machine learning algorithms to support decisions on location, fertilizer, and the best time to harvest their crops.

The machine learning algorithms analyze the data sent from drones and satellites to

  • Predict weather patterns and crop yields.
  • Determine the required amount of water.
  • Evaluate the crop’s vulnerability to disease or pests.
  • Monitor anything that would interfere with the harvest.

ML algorithms also pave the way for automating farming operations, including more efficient irrigation systems.

Overcoming Barriers to Data Science Adoption in Agriculture

While data science is increasingly being adopted in agriculture, it remains in the early innings, which creates some challenges. Big data analytics is one area that has a long way to go.

Data accuracy is paramount in agriculture, as is the standardization of that data. However, the industry has yet to develop a single format or standard, which can create challenges when examining information.

Drone and satellite data might be delivered in multiple formats across various platforms. This places the onus on the farmer to ensure that these systems are compatible with their own, which requires time, education, and investment.

Analyzing big data also requires particular skill sets that not all corners of the farming community might possess, adding to the industry’s hurdles. Collecting big data is expensive and can be cost-prohibitive, making it challenging for small growers to benefit from the technology.


Now that the agriculture industry has embraced data science, technology is here to stay, and the future looks bright. The farming community has already come to depend on innovations such as drones, satellites, and machine learning algorithms to help them in activities ranging from water management to crop predictions.

The role of data science in agriculture is only likely to increase in the coming years as farmers contend with the effects of climate change, a growing population, greater food demand, and fewer resources. The more they can make sense of the data, the more profound the role that data science will likely play in a more sustainable future for farming and agriculture.

Additional Blog