High-precision Plant Stand Count for Corn, Sunflower and Sugar Beet by a Drone and AI

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. New technologies like drones and AI leverage the opportunity to make Agri operations smarter and more efficient. With this innovative approach, growers can now receive accurate data, make timely decisions and sustainably maximise the yield. Surprisingly, this is not as complicated or costly as it might seem. 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. Why and when do you need a precise plant stand count? There are situations when a low accuracy report is acceptable, but it is absolutely essential to have a precise one if you aim to: Check the sowing quality, especially if you are producing seeds; Understand zones of varying productivity in the fields; Receive accurate data during R&D projects; Estimate the yield precisely in the early stages; Spot rogues; Make timely decisions, i.e., partially replant the field; Increase the yield potential to meet the production goals. What are benefits of automated plant stand count? On the automatic report generated by Proofminder platform, you can see Plant & row density; Precise plant stand count; Each plant is marked on the field with precise coordinates; Plant distinguished by phenotype, in this case – male and female plants of hybrid corn are marked with a different colour; Zoom-in feature to analyse specific zones, rows or plants. When is the best time for plant stand count using a drone and AI? 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​ Capturing images by a drone – instructions and tips 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. Proofminder works on novel ways to capture images and foresee the possibility in the near future to capture up to double of this area per day by a Phantom 4 drone.   There are ways to extend the area of image capturing in the near future. Proofminder team foresees this possibility and works to double the area captured per day by a Phantom 4 drone. Figure 3 Shooting images for plant stand count by DJI Phantom 4​ Things to avoid; the Top-10 common mistakes in drone footage: Wrong exposure setting, not properly assessing the weather, resulting in over- or underexposure. Overexposure is more of a problem than underexposure, so if you need to choose between cloud and sunny, and you are not sure, you can safely go for sunny. Too much wind or unstable weather conditions result in blurry images. Not equipped with sufficient memory cards, make sure you have at least a 64 GB card for ~40-50 hectares of land. Not enough batteries and/or chargers to fly continuously during the day. Shooting after rain may require some recalibrations because the plant on the wet soil may not be visible enough, keep this in mind. Not flying with the right amount…

Introducing Drone Technology for Precise Agricultural Cotton Production

Background Cotton, the most important fiber crop in the world, plays a significant role in the economics of many countries. In the crop year 2020/2021,[1] China, India, and the United States ranked as the top three producers of cotton. China ranked second behind Australia in average yield – 1,879 kg per hectare. High yield is not only due to a high mechanization rate but also precise management by cotton farmers. Cotton farmers make many decisions across the growing season – sowing, reseeding, fertilization, growth regulation, pesticide spraying, defoliant application, etc.   In order to improve crop scouting efficiency, reduce chemical usage, and boost yield, cotton farmers in China’s Xinjiang Province are adopting drone technology in both field scouting and chemical application. The Mission Gongxu Chen is a cotton farmer in Xinjiang, China, who manages 300ha of cotton fields. Formerly, in order to optimize yield, he applied Mepiquat chloride, a chemical used as a growth regulator in cotton, 6-7 times in one growing season to tune the height and canopy of the cotton plants. Before each application, Gongxu walked the field on foot and selected around three sample locations in every ten hectares to take individual height measurements. This year, he used DJI’s P4 Multispectral drone to help with field scouting. In one 25-minute flight, the P4 Multispectral drone captured 45 hectares of imagery. The imagery was post-processed in DJI Terra software to generate RGB mapping and vegetation indices, including NDVI. In less than one day, Gongxu received an NDVI growth map of his 300ha cotton farm, showing the growth variation of the whole field. With this map, Gongxu was able to segment his fields into growth zones and strategically place sampling locations for further inspection. RGB imagery and NDVI map of 45ha cotton fields An NDVI map is also used for precise spraying. Based on the NDVI map, Gongxu modified his regular flat-rate application method to variable rate application (VRA) by generating a Mepiquat chloride prescription map in which only the area with over-growth was assigned a spraying rate while the rest were left as non-spray. This prescription map was then downloaded to the DJI Agras T30 drone for execution. Prescription map of Mepiquat chloride T30 in mission Similarly, in the later growth stage, Gongxu generated a VRA prescription for foliar fertilization to boost the growth of the weaker regions and improve the homogeneity of the field. Improved field homogeneity after VRA, as shown by an NDVI map Conclusion The success of new technology adoption interests cotton farmers in the region. Gongxu plans to expand his business to provide precision agriculture services to neighboring farms, scouting cotton fields using P4 Multispectral and offering variable rate application with Agras drones. In his world, “Investment in drone technology clearly improves yield potential and reduces farming costs. By providing services, more farmers can benefit from innovative precision agriculture solutions.” In order to improve crop scouting efficiency, reduce chemical usage, and boost yield, cotton farmers in China’s Xinjiang Province are adopting drone technology in both field scouting and chemical application.