Abstract
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.
Highlights
Corn, together with wheat and rice, is one of the most important cereals in the world; it supplies nutritional elements to human beings and animals, being a basic raw material of the transformation industry to produce starch, oil, proteins, alcoholic drinks, food sweeteners and fuel [1]
As far as we know the normalized cross-correlation and template comparison, in combination with the CIELAB color space has not been reported in the literature for the corn plant count, this paper investigates whether this relatively simple methodology can be accurate in corn plant counting
Automatic counting of corn plants through comparing templates with normalized cross-correlation (NCC) was done and validated. This was done in the first growth stages of the crop using RGB images acquired with an unmanned aerial vehicle
Summary
Together with wheat and rice, is one of the most important cereals in the world; it supplies nutritional elements to human beings and animals, being a basic raw material of the transformation industry to produce starch, oil, proteins, alcoholic drinks, food sweeteners and fuel [1]. Planting density (number of plants per unit area), the number of cobs per unit area, the number of kernels per cob and kernel weight are the grain yield components that have an impact on the reachable yield of corn [2]. The determination of planting density or the number of plants per hectare is important information to evaluate the physiological characteristics of the crop, and Agronomy 2020, 10, 469; doi:10.3390/agronomy10040469 www.mdpi.com/journal/agronomy. Yield can be better estimated by including the precise number of plants in the models, to identify the optimum planting density to manage production in corn growing. Camera focal length and flight height define the image scale [9], It is of great importance to define the optimal pixel size according to the specific objectives and the crop characteristics [10]. RGB sensors capture radiation in the visible electromagnetic spectrum of red, green and blue bands, while common multispectral sensors are widely used due to obtaining spectral information in the red band, the near-infrared and red edge for monitoring vegetation, on the other hand, hyperspectral sensors capture reflectance in several narrow spectral bands (5–10 nm) [13,14]
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