The use of drones has become widespread in agriculture, and it is associated with unique opportunities and challenges. The most common role of drones in agriculture is as remote sensing platforms to assess and monitor crops, but emerging agricultural applications include precision distribution of chemicals and biological control agents.

By implementing drone technology, farms and agriculture businesses can improve crop yields, save time, and make land management decisions that will improve long-term success. Agricultural drones allow farmers to obtain access to a wealth of data they can use to make better management decisions.

One of the great advantages of drone technology is the effectiveness of large-scale crop and acreage monitoring. In the past, satellite or plane imagery was used to help get a large-scale view of the farm, while helping to spot potential issues. 

However, these images were not only expensive but lacked the precision that drones can provide. Today, you cannot only obtain real-time footage but also time-based animations, which can illuminate crop progression in real-time. With drone mapping and surveying, technology decisions can now be made based on real-time data, not outdated imagery, or best-practice guesswork. 

With near infrared (NIR) drone sensors, farmers can actually determine plant health based upon light absorption, giving invaluable insight of the overall farm health. Such tools make it easy to collect information like overall crop and plant health, land distribution based on crop type, crop life cycle, and detailed GPS maps of current crop area.

Yield Assessment

A recently developed decision-making tool makes use of artificial intelligence known as Deep Learning alongside drone-taken images of the crops to calculate stem numbers, and map where they occur. The technique has been introduced by AHDB-funded PhD student Joseph Mhango from Harper Adams University.

Mhango said: “Agronomists need to know stem population to be able to model tuber numbers. Over the past two years, we have been developing some techniques based on artificial intelligence to start solving the problem of how best to estimate the differences in stem density across a potato field at full canopy, normally at 70 days after planting.”

By analyzing vegetation indices using regular red, blue and green wavelengths taken by the drone, Joseph discovered that meristematic tips of potato plants can be counted and used to represent stem tips. Deep Learning was then used to develop a robust model for estimating stem numbers, which can be used to produce a heat map of stem population density across a field.

The tool is primarily aimed at facilitating harvesting decisions, so that areas with greater numbers of tubers can be left more time to bulk, while those with fewer, larger tubers are harvested first.

 “Previously trained models show that where there are more stem numbers per area of ground, higher numbers of tubers are to be expected at a cost of the average tuber size,” Mhango explained.

He noted that growers are well familiar with the relationship between potato stem population and tuber yield as well as size distribution, and decisions on harvest timings are normally based on a number of yield digs across the field.

You can read the rest of this article in the Spring Issue of Potato Business Digital magazine, which you can access by clicking here.