Detection method for the guidance directrix of daylily in the field environment
Abstract
The daylily (Hemerocallis citrina Baroni) is an herbaceous perennial whose flowers are rich in nutritional and functional components. It is typically cultivated in rows but harvested manually, a labor-intensive process that this study aims to automate by developing a robotic harvesting system. A critical component of such a system is autonomous navigation, which poses significant challenges in unstructured field environments due to changing natural light, randomly distributed weeds, and varying inter-row density. To address these challenges, this study adopted vision-based navigation technology and proposed a guidance directrix detection algorithm. The proposed approach begins with converting the color model from RGB to HSV to decouple the brightness, thereby mitigating the impact of natural light variations. Morphological dilation is applied to suppress noise from weeds in the inter-row regions. Furthermore, an innovative coarse segmentation strategy based on hue and saturation is introduced to handle the problem of different sparsity in the inter-row. Finally, the inter-row axis is accurately extracted by employing concepts from physics, namely, the center of mass and moment of inertia. Experimental results demonstrate an accuracy of 98.25%, a recall of 100%, an average navigation processing time of 8.7521 ms, and a compact model size of only 18 KB. These findings empirically confirm that the proposed approach achieves high precision and real-time performance under unstructured, uncertain, and dynamically changing field conditions. Additionally, the algorithm operates with high computational efficiency and requires neither expensive hardware nor large-scale training datasets.
Keywords: visual navigation, robotic daylily harvester, guidance directrix, inter-row axis
DOI: 10.25165/j.ijabe.20261902.9706
Citation: Feng J H, Zhang Z L, Li D Q, Li G A, Huang Z B, He X H, et al. Detection method of the guidance directrix of daylily in the field environment. Int J Agric & Biol Eng, 2026; 19(2): 262–271.
References
[1] Yao Z X, Zhao C J, Zhang T H. Agricultural machinery automatic navigation technology. iScience, 2023; 27（2）: 108714.
[2] Marchant J A. Tracking of row structure in three crops using image analysis. Computers and Electronics in Agriculture, 1996; 15（2）: 161–179.
[3] Åstrand B, Baerveldt A. A vision based row-following system for agricultural field machinery. Mechatronics, 2005; 15（2）: 251–269.
[4] Choi K H, Han S K, Han S H, Park K H, Kim K-S, Kim S. Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields. Computers and Electronics in Agriculture, 2015; 113: 266–274.
[5] Li J B, Zhu R G, Chen B Q. Image detection and verification of visual navigation route during cotton field management period. Int J Agric & Biol Eng, 2018; 11（6）: 159–165.
[6] Guerrero J M, Guijarro M, Montalvo M, Romeo J, Emmi L, Ribeiro A, et al. Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Systems with Applications, 2013; 40（2）: 656–664.
[7] Si Y S, Jiang G Q, Liu G, Gao R, Liu Z F. Early stage crop rows detection based on least square method. Transactions of the CSAM, 2010; 41（7）: 163–167, 185.
[8] Song Y, Liu Y B, Liu L, Zhu D Q, Jiao J, Chen L Q. Extraction method of navigation baseline of corn roots based on machine vision. Transactions of the CSAM, 2017; 48（2）: 38–44.
[9] Liang X H Z, Chen B Q, Wei C J, Zhang X C. Inter-row navigation line detection for cotton with broken rows. Plant Methods, 2022; 18: 90.
[10] Zhang X C, Chen B Q, Li J B, Fang X, Zhang C L, Peng S B. Novel method for the visual navigation path detection of the jujube harvester autopilot based on image processing. Int J Agric & Biol Eng, 2023; 16（5）: 189–197.
[11] Billingsley J, Schoenfisch M. The successful development of a vision guidance system for agriculture. Computers and Electronics in Agriculture, 1997; 16（2）: 147–163.
[12] Søgaard H T, Olsen H J. Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture, 2003; 38（2）: 141–158.
[13] Liu W, Hu J P, Liu J X, Yue R C, Zhang T F, Yao M J, et al. Method for the navigation line recognition of the ridge without crops via machine vision. Int J Agric & Biol Eng, 2024; 17（2）: 230–239.
[14] Han S, Zhang Q, Ni B, Reid J F. A guidance directrix approach to vision-based vehicle guidance systems. Computers and Electronics in Agriculture, 2004; 43（3）: 179–195.
[15] Feng J H, Li Z W, Yang W, Han X P, Zhang X L. Detection navigation baseline in row-following operation of maize weeder based on axis extraction. Int J Agric and Biol Eng, 2020; 13（5）: 181–186.
[16] Lin Y K, Chen S F. Development of navigation system for tea field machine using semantic segmentation. IFAC-PapersOnLine, 2019; 52（30）: 108–113.
[17] Song Y, Xu F Y, Yao Q, Liu J L, Yang S. Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera. Information Processing in Agriculture, 2023; 10（4）: 475–490.
[18] Chen Z Y, Cai Y H, Liu Y X, Liang Z P, Chen H, Ma R J, et al. Towards end-to-end rice row detection in paddy fields exploiting two-pathway instance segmentation. Computers and Electronics in Agriculture, 2025; 231: 109963.
[19] Xie X L, Li Y C, Zhao L J, Jin X, Wang S S, Han X B. Method for the fruit tree recognition and navigation in complex environment of an agricultural robot. Int J Agric & Biol Eng, 2024; 17（2）: 221–229.
[20] Lai X D, Huang J Z, Mo Y M, Li H W, Dong T Y, Wu T, He D Q. A lightweight deep learning model for synchronized crop stem detection and row segmentation at the seedling stage: Exploring their contribution to agricultural navigation line extraction. Computers and Electronics in Agriculture, 2026; 243: 111385.
[21] Gonzalez R, Woods R E. Digital image processing. Boston: Addison-Wesley Publishing Company. 1992; 793p.
[22] Maragos P, Schafer R. Morphological filters-part I: their set-theoretic analysis and relations to linear shift-invariant filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1987; 35（8）: 1153–1169.
[23] Haralick R M, Shapiro L S. Computer and robot vision. New Jersey: Addison-Wesley Publishing Company. 1992; 672p.
[24] Ding S. Research on axis extraction methods for target’s attitude measurement. Master of Engineering dissertation. Changsha: National University of Defense Technology. 2013; 70p.
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