Abstract
Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.
Highlights
Introduction published maps and institutional affilTo increase economic benefits and reduce environmental impact, precision farming is recommended to farmers [1]
The objectives of this study are as follows: (i) to design a new method according to the least squares fitting-based method used in field images for crop row detection in orthomosaic unmanned aerial vehicle (UAV) images and (ii) to test the performance of the method proposed in this study by comparison with the Hough transform-based method
For the wheat field experiment, the analysis of variance (ANOVA) method was used to investigate the effect of water and nitrogen on wheat biomass
Summary
Introduction published maps and institutional affilTo increase economic benefits and reduce environmental impact, precision farming is recommended to farmers [1]. Precision agriculture requires the application of fertilizer, pesticides, irrigation and other field management practices according to the needs of crops [2]. An accurate prediction of the crop growth status in the field, followed by the generation of site-specific management practice maps, is very important [3,4,5]. Most crops are grown in rows to increase light exposure, enhance gas exchange and facilitate weeding and fertilization. With respect to the generation of a precision site-specific management practice map, the location of the crop rows in the field must be considered. Many studies have been conducted using field images obtained by digital cameras or multispectral sensors mounted on autonomous tractors or robots to recognize the location of crop rows iations
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