The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields.

中文翻译：

---

利用无人机拍摄的图像和模板的互相关性对玉米植物进行数字计数

植物的数量或种植密度是影响玉米作物产量的关键因素。本研究工作的目的是使用安装在无人机（UAV）上的传感器获得的图像对玉米植物进行计数。建立了五个水平的氮肥水平（140、200、260、320和380 kg / ha）的实验，并进行了四次重复试验，得到了20个试验田。图像是在30 m的飞行高度播种（DAS）的23、44和65天后，使用配备12、16和20兆像素RGB传感器的两架无人机（Canon PowerShot S100_5.2，Sequoia_4.9，DJI FC6310_8.8）。通过归一化互相关（NCC）对a *中的四个，八个和十二个植物样品或模板进行计数CIELAB颜色空间的通道，因为它代表允许植物分割的绿色。对于像素尺寸为0.49 cm的像素，平均精度为99％，在44 DAS下的平均误差为2.2％，确定系数为0.90。在23和44 DAS时获得的精度值超过91％，通过数字和视觉计数的植物之间的平均误差为±5.4％。相关估计中样本或模板数量的增加提高了计数精度。当植物没有重叠并且没有杂草时，在作物的第一个生长阶段就达到了良好的精度。使用传感器和无人驾驶飞行器，可以确定田间幼苗的出现，并更精确地评​​估种植密度，并获得更准确的信息以更好地管理玉米田。