Data Science in Agriculture

Data Science represents a real revolution for agriculture: the oldest human industry. Learn how data is being used to modernize and improve the way food is grown…

Illustration of Data science in agriculture sector

In 2050, the world population will reach 9.3 billion human beings. By then, according to the Food and Agriculture Organization ( FAO ), agriculture will need to increase by 70% globally to meet demand.

This challenge is all the greater because the resources to meet it are limited. Land, water, and fertilizers are already running out to feed all of humanity.

After the industrial age, driven by heavy machinery and seed science, we are about to enter the era of agriculture 3.0 thanks to Data Science. Data science is indeed a valuable asset for finding new solutions to current problems in agronomy.

Modern technologies like the Internet of Things make it possible to collect a lot of data on soils, water, and minerals on farms and store them in centralized systems that promote sharing.

This data can be combined with data from external sources such as satellites or weather stations. They can then be analyzed to gain insight into opportunities for process optimization.

Thus, Data Science is now disrupting the way farmers and farmers make decisions throughout the production cycle from seed planting to harvest. It increases productivity, enables sustainable agriculture, and offers more transparency to consumers who care about where their food comes from. Here's how data science is transforming agriculture and agronomy.

Why is Data Science essential for agriculture?

Agriculture is the basis of human civilization and has evolved greatly through the invention of new tools, methods, and machines. This evolution continues today.

Until now, farmers had to rely solely on their intuition to make decisions. If you make a mistake, an entire season's harvest can be wasted.

Data Science solves the problem by allowing farmers to rely on data to make better decisions. It also offers the opportunity to exploit the vast volumes of data generated by IoT sensors and via the Internet.

It also responds to new demands from consumers, who want to eat better and know where and how their food has been produced, packaged, modified, and distributed. Finally, it is a valuable tool for increasing the production of food at a lower cost and feeding all of humanity.

How do farmers collect data?

Different sources allow farmers to aggregate data. IoT sensors placed on farms make it possible in particular to collect information on soil nutrients, water content, or air permeability.

This data can be combined with data from external sources, such as temperature or precipitation statistics. In addition, new technologies such as spectroscopes make it possible to measure the quality of the soil or the quality of the fruits and vegetables produced on the farm.

Data Science and Precision Agriculture

By analyzing the data thus collected, farmers can measure the quality of their production down to the molecular level. Data Science makes it possible to practice what is called precision agriculture.

This concept consists of using only the quantity of resources necessary, with a view to sustainable development and the elimination of waste. Mineral, fertilizer, and water requirements can be measured exactly for each plant. This method saves a large number of resources, and by extension reduces production costs.

In horticulture, it is possible to attach RFID chips to animals for better tracking. If a sick animal is identified, the farmer can immediately treat it.

Agricultural pests and other diseases can be disastrous for farmers. However, the misuse of pesticides can have terrible effects on humans, plants, and animals.

Better use of Pesticides

Now, several companies are recruiting Data Scientists to develop analytical platforms capable of determining when to use pesticides and to what extent. As an example, we can cite the Brazilian firm Agrosmart whose technology uses AI and IoT to identify insects threatening a plant.

Farmers benefit from a report and can rely on it to manage the use of pesticides in an economical way, with a minimized impact on the environment. Similarly, the Israeli startup Saillog has created a mobile application informing farmers about diseases affecting their plantations or those of surrounding farms.

Adaptation to climate change

The agricultural sector is undoubtedly the most impacted by climate change. Fortunately, Data Science makes it possible to deal with this.

In Taiwan, in the rice fields, IoT sensors are now used to collect information on the plantations. These data are used by farmers to optimize production cycles, at a time when climate change makes this task particularly complex. Indeed, the traditional calendar is no longer a reliable source to rely on.

Scientists also use soil data to better understand how soils contribute to climate change by releasing greenhouse gases and how to adapt to them.

Harvest Prediction

To help farmers, IBM has developed a platform to predict the volume of corn harvested two or three months in advance. This avoids unpleasant surprises.

Similarly, researchers at the University of Illinois are using seasonal predictions and satellite data to make end-of-season predictions earlier than usual. This method is even more accurate than real-time data from the United States Department of Agriculture.

Data Science and agriculture: the challenges to be met

Despite the benefits of Data Science for agriculture, there are still many challenges to overcome. First of all, the implementation of this science is difficult because this industry is generally resistant to change.

Farmers are reluctant to change methods because the costs can be very high if they fail. The digital transition also represents a significant investment, and only the largest farmers can afford it.

Installing sensors and a centralized server for data storage is also very expensive. Data is collected in various formats, at different time intervals. It is, therefore, necessary to convert them in order to be able to compare them with each other.

It is also essential to share the data with other farmers so that the volume is sufficient, which also involves risk in terms of security and confidentiality. These are the main obstacles to the implementation of Data Science in agriculture and agronomy.

More Reading | Reference

[1] Datascientest 

[2] Wikipedia 

[3] MIT News

[4] Towardsdatascience

[5] tdwi

[6] Kaggle