Case Study: How Drones Analyze Crops for Better Yields

Utilizing drone mapping to analyze animal feed crops

Mexican company Azulda, provides high-value drone data solutions to local growers in the La Comarca Lagunera region. This northern region is the largest producer of milk and dairy in Mexico.

According to a recent study, 35,290 thousand hectares were harvested in La Laguna last year. There is an evident demand to offer more efficient methods to produce large amounts of livestock feed in the industry. Consequently, Azulda is the first to implement precision agriculture methods in the area and making further strives to reach everyday agriculture 4.0 methods.

The term agriculture 4.0 is a term commonly associated with leading trends facing the industry. Currently this means a bigger focus on the loT (the internet of things), precision agriculture and the use of big data to make decisions. These cutting-edge technologies are determined to develop ways of sustainably feeding the world in face of climate change and mass population.

Project Details

Azulda worked with the Establo Santa María dairy farm to analyze feed crops. The goal of the project was to verify the quality of crop (oat) in two different plots of soil with two-different treatments. 

With over 400 hectares to produce livestock feed, it is crucial to know the quality and quantity of animal feed. This data was critical to determine which of the different treatments would perform better. 

Using PIX4DFields to create a map, growers were able to indicate the average values of vegetation indexes associated with biomass and chlorophyll content such as NDRE and NDVI across the area and how it’s affected by treatments.

José Alfredo, Manager at Azulda expressed, “Our work wouldn't have been possible without PIX4Dfields. The fast processing and tools such as the zonification by the arithmetic mean of vegetation indexes allowed us to deliver a high-quality zonation map”.

Azulda credited the speed, quality of data output, index libraries, and the ability to capture radiometric and sun corrections as differentiators to any other providers. The acquired data and image analysis allowed the grower to make informed decisions and plan their next growing season accordingly in order to achieve optimal production.

Source:

Drone mapping for analyzing animal feed crops