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Navigation Line Extractionin in Pear Orchard Based on MLKA and Temporal RANSAC Fusion
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Jianjun ZHOU1, Quanle LIU1, Lei CAO1, Lin FAN1, Quan QIU2
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 657 - 665
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 657-665
• Agricultural Informatization Engineering •
Navigation Line Extractionin in Pear Orchard Based on MLKA and Temporal RANSAC Fusion
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Jianjun ZHOU1, Quanle LIU1, Lei CAO1, Lin FAN1, Quan QIU2
Affiliations
  • 1.College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
  • 2.College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.060
Outline
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Traditional visual navigation methods struggle to handle interference from varying lighting conditions and weed occlusion in complex pear orchard environments. An improved navigation line extraction method was proposed based on YOLO v8 to address this challenge. The process integrated a multi-scale large kernel attention (MLKA) module into the YOLO v8 model to enhance the perception of trunk features. A multi-frame feature point fusion mechanism was designed. By recording and utilizing feature points detected in five consecutive frames, this mechanism effectively compensated for the problem of insufficient feature points in single-frame detection. Additionally, the random sample consensus (RANSAC) algorithm was introduced to denoise the feature points of the left and right tree rows, respectively, and the least squares method was used for the two side tree rows' line fitting. The navigation line was ultimately generated by calculating the angle bisector of the fitted lines from both sides of the tree rows. Experimental results showed that the improved model achieved a precision of 89.8%, a recall of 79.9%, and a mean average precision (mAP50-95) of 55.1% in tree trunk detection tasks under various lighting conditions and weed occlusion scenarios. The navigation line generated by combining multi-frame feature point fusion with RANSAC denoising exhibited an average angular deviation of only 1.17° from the manually annotated reference navigation line, an average position deviation of 20.40 pixels, and an average root mean square deviation of 0.27. The research result can provide a low-cost and highly adaptable technical solution for visual navigation in pear orchard environments.

pear orchard  /  automatic navigation  /  YOLO v8  /  MLKA  /  RANSAC algorithm  /  multi-frame feature point fusion
周建军, 刘泉乐, 曹磊, 樊林, 邱权. 基于MLKA与时序RANSAC融合的梨园导航线提取方法. 农业机械学报, 2025 , 56 (12) : 657 -665 . DOI: 10.6041/j.issn.1000-1298.2025.12.060
Jianjun ZHOU, Quanle LIU, Lei CAO, Lin FAN, Quan QIU. Navigation Line Extractionin in Pear Orchard Based on MLKA and Temporal RANSAC Fusion[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 657 -665 . DOI: 10.6041/j.issn.1000-1298.2025.12.060
Year 2025 volume 56 Issue 12
206
98
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Article Info
doi: 10.6041/j.issn.1000-1298.2025.12.060
  • Receive Date:2025-05-15
  • Online Date:2026-09-30
  • Published:2025-12-25
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  • Received:2025-05-15
  • Revised:2025-07-10
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Affiliations
    1.College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
    2.College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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