In this paper an efficient crop row detection method is proposed for vision-based navigation for agriculture robots. In the proposed method, no low level features (such as edges and middle lines of the images) are nee...
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ISBN:
(纸本)9781479958368
In this paper an efficient crop row detection method is proposed for vision-based navigation for agriculture robots. In the proposed method, no low level features (such as edges and middle lines of the images) are needed. So the complex algorithms for edging and matching (e.g. the Hough transform) are avoided, which greatly saves the computation loads. Instead, a flexible quadrangle is defined to detect the croprows. The proposed method moves, extends or shrinks the flexible quadrangle to localise the croprows in the captured frames. The experiments demonstrate that the proposed method is effective with high time efficiency and detection accuracy.
In precision agriculture, activities such as selective spraying of agrochemicals are essential to maintaining high productivity and quality of agricultural products. The use of unmanned aerial vehicles (UAVs) to perfo...
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In precision agriculture, activities such as selective spraying of agrochemicals are essential to maintaining high productivity and quality of agricultural products. The use of unmanned aerial vehicles (UAVs) to perform this activity reduces soil compaction, compared to the use of heavy machinery, and helps to reduce the waste of these artificial substances through a punctual and self-regulating application. This work proposes an entire guiding system for use on UAVs (hardware and software) based on image processing techniques. The software part consists of two algorithms. The first algorithm is the croprowdetection which is responsible for the correct identification of the croprows. The second algorithm is the Line Filter that is responsible for generating the driving parameters sent to the flight controller. In the field experiments performed on the proposed hardware, the algorithm achieved a detection rate of 100% of the croprows for images with resolutions above 320 x 240. The system performance was measured in laboratory experiments and reached 31.22 FPS for images with small resolution, 320 x 240, and 1.63 FPS for the highest resolution, 1920 x 1080. The main contribution of this work is the design and development of an entire embedded guidance system composed of a hardware and software architectures. Other contributions are: the proposed filter for the image pretreatment;the filter to remove the false positive lines;and the algorithm for generating the guiding parameters based on detected croprows.
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