Plant stand count is an essential task in yield management. It allows growers to estimate the plant population, density, germination rate, and plant health and make timely decisions that finally affect the yield. Common manual methods of plant stand counting have helped growers for decades. They are based on visual inspection and plant calculation on small pre-defined field areas. However, these methods are laborious and far from accurate. Fragmented plant stand count does not provide the complete picture, and problem areas with uneven emergence or weeds might be overlooked. The lack of information on the field eventually leads to a waste of resources and less profitable decisions.
This article covers precise plant stand count using an off-the-shelf drone and Proofminder’s trained AI algorithm for accurate yield assessment and the following insights on the field. You will find practical tips on image collection and recommended approach for corn, sugar beet and sunflower, but the information is also useful for other field crops, vegetables and orchards. If you have a drone or considering buying one to turn a tedious task into an interactive process and get a high-precision result, keep reading. You will find drone requirements, flight tips and common mistakes, and learn how to get a precision stand count report in a few hours with an innovative AI farming platform.
There are situations when a low accuracy report is acceptable, but it is absolutely essential to have a precise one if you aim to:
Estimating the number of plants and their density is crucial for early-season yield management. The accurate information here is a chance to save the yield if something goes wrong and improve the harvest. To gather proper images for further analysis, consider the tips about plants and the weather.
The plant should be big enough to be seen from the air, but the leaves are not yet too close to each other to distinguish plants and estimate the density. As an example, for the precise stand count of corn, the plant should have about 3-7 leaves (V3-V7 vegetation stages). The weather should be stable during the footage, thus the lens can adapt to the conditions whether it is sunny or cloudy. Also, it should not be too windy, note that the wind speed may greatly vary depending on the altitude. Which altitude is right for a stand count? Find below!
Figure 1 – Corn field
Figure 2 – Manual plant stand count of corn
The ideal resolution for plant stand count by a drone and intelligent software depends on the plant and the goal. For precise stand calculation of corn, sunflower, sugar beet, and some other field crops and vegetables would be 0.8 cm per pixel or less. What does it imply, and what kind of drone is suitable? The widely available DJI Phantom 4 Pro V.2. can be a good entry-level option for that job, similarly, the DJI Phantom 4 RTK is also a great option if you want a professional drone with high precision positioning. You will need to fly at 18-30 meter altitude to get the indicated resolution. Be aware that some of the Integrated controllers (the Plus versions) limit the flight altitude to 25m above the ground so if you want to count small crops and fly low, you would rather choose the simple controller and instruct the drone from your mobile or tablet. The ideal speed to capture detailed images would be between 3-5 m/s depending on the altitude and the wind conditions. Using this drone, you can proceed at about 25-30 hectares per day if you have enough batteries; mind you: you can charge them on the site. At Proofminder, we are working on novel ways to do this image capturing and foresee the possibility in the near future to capture up to double of this area per day by a Phantom 4 drone.
Figure 3 – Shooting images for plant stand count by DJI Phantom 4
Figure 4 – The process of drone footage for precise stand count
Figure 5 – The shape-file of the field
Following the instructions will result in lots of useful data and good images for further analysis and insights about the field and plants. What can you, as a grower, do with the collected images? There are a couple of ways – as an illustration, to analyse it manually, which is again time-consuming and subjective or use Artificial Intelligence, which can do the job quickly and accurately. The AI-powered platform can create an orthomosaic, an automatic plant stand count report and mark issues on the field that are not visible or not humanly possible to discover in traditional methods.
Images below show what your plant stand count can look like on the Proofminder platform.
On the automatic report generated in a system, you can see
Figure 6 – A plant stand count report on the Proofminder platform
Figure 7 – A plant-level view of a stand count report on the Proofminder platform