冬季猕猴桃树单木骨架提取与冠层生长预测方法  

Individual Tree Skeleton Extraction and Crown Prediction Method of Winter Kiwifruit Trees

作　　者：李政凯 于嘉辉 潘时佳 贾泽丰 牛子杰[1] LI Zhengkai;YU Jiahui;PAN Shijia;JIA Zefeng;NIU Zijie(College of Mechanical and Electronic engineering,Northwest A&F University,Xianyang 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西咸阳712100

出　　处：《智慧农业（中英文）》2023年第4期92-104,共13页Smart Agriculture

基　　金：国家自然科学基金联合基金重点项目(U2243235);国家重点研发计划(2022YFD1900802)。

摘　　要：[目的/意义]猕猴桃果树生长重叠明显,树冠结构复杂,利用传统方式无法实现果树单木骨架提取与冠层预测,为对密集栽培的猕猴桃果园进行高效无损监测并获取果树生长参数,本研究利用冬季简单树形进行骨架提取,并集成深度学习与数学形态学方法,提高单木骨架预测精度,提出了一种融合骨架信息的冠层分割方案。[方法方法]采用低成本无人机图像获取高分辨率数据支持,改进PSP-Net语义分割模型,引入数学形态学处理提取单木骨架并优化骨架连续性,以优化单木骨架为先验实现冠层分割。[结果与讨论]优化骨架提取精度可达95%以上,相较于传统方式精度提高约15.71%,像素准确率(Pixel Accuracy,PA)值达95.84%,平均交并比(Mean In-tersection over Union,MIo U)值达95.76%,冠层分割加权得分(Weighted F1 Score,WF1)达94.07%左右;而冠层预测像素准确率PA可达95%以上,冠层分割WF1达95.76%左右,与直接利用原始骨架相比,优化骨架提高了冠层分割的PA为13.2%,MIo U为10.9%,WF1为18.4%,显著改善了分割指标。[结论]该研究为高效监测猕猴桃园以获取果树数据提供了可靠技术支撑,并为高效、低成本的果园精细化管理提供了全新的技术方案,具有重要的应用前景。[Objective]The proliferation of kiwifruit trees severely overlaps,resulting in a complex canopy structure,rendering it impossible to extract their skeletons or predict their canopies using conventional methods.The objective of this research is to propose a crown segmentation method that integrates skeleton information by optimizing image processing algorithms and developing a new scheme for fusing winter and summer information.In cases where fruit trees are densely distributed,achieving accurate segmentation of fruit tree canopies in orchard drone images can efficiently and cost-effectively obtain canopy information,providing a foundation for determining summer kiwifruit growth size,spatial distribution,and other data.Furthermore,it facilitates the automation and intelligent development of orchard management.[Methods]The 4-to 8-year-old kiwifruit trees were chosen and remote sensing images of winter and summer via unmanned aerial vehicles were obtain as the primary analysis visuals.To tackle the challenge of branch extraction in winter remote sensing images,a convolutional attention mechanism was integrated into the PSP-Net network,along with a joint attention loss function.This was designed to boost the network's focus on branches,enhance the recognition and targeting capabilities of the target area,and ultimately improve the accuracy of semantic segmentation for fruit tree branches.For the generation of the skeleton,digital image processing technology was employed for screening.The discrete information of tree branches was transformed into the skeleton data of a single fruit tree using growth seed points.Subsequently,the semantic segmentation results were optimized through mathematical morphology calculations,enabling smooth connection of the branches.In response to the issue of single tree canopy segmentation in summer,the growth characteristics of kiwifruit trees were taken into account,utilizing the outward expansion of branches growing from the trunk.The growth of tree branches was simulated by using morpho

关 键 词：果树骨架提取 冠层预测 深度学习 机器视觉 数字图像处理 无人机遥感 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]