| 203 | 0 | 260 |
| 下载次数 | 被引频次 | 阅读次数 |
苹果作为我国优势林果作物，疏果是其调控产量、提升品质的关键环节。在传统苹果园向现代化苹果园转型的过程中，树形结构与种植模式发生根本性变革，传统依赖高强度人工经验的疏果方式已难以适应规模化、标准化与高效益的生产需求。本文梳理了苹果疏果技术的演变路径，首先综述了以人工经验、化学药剂及半机械装置为代表的传统疏果技术特点与研究现状，并指出其存在效率低、精度差以及适应性不足等问题。进而，重点聚焦现代智能疏果技术，深入剖析了基于深度学习的果实识别、果实定位以及关键末端执行器的设计原理与研究进展。针对当前智能疏果技术仍面临幼果识别与定位精度不足、果实机械损伤突出、系统稳定性差及研发制造成本高昂等核心挑战，提出了未来通过创新算法、优化机械结构与设计以推动技术落地的发展方向。本文旨在为现代化苹果园智能化疏果技术的持续研发与产业应用提供系统的理论参考。
Abstract：As a predominant fruit crop in China,apple thinning represents a critical procedure for yield regulation and quality enhancement.During the transformation from traditional orchards to modernized cultivation systems,fundamental changes have occurred in tree architecture and planting patterns,rendering conventional thinning methods reliant on intensive manual expertise increasingly inadequate to meet the demands of scaled,standardized,and high-efficiency production.This paper systematically examined the evolutionary trajectory of apple thinning technologies,initially reviewing the characteristics and research status of traditional approaches represented by manual expertise,chemical agents,and semi-mechanical devices,while highlighting their limitations in efficiency,precision,and adaptability.Subsequently,the study focused on modern intelligent thinning technologies,providing an in-depth analysis of the design principles and research advancements in deep learning-based fruit recognition,fruit positioning,and key end-effector development.Addressing the core challenges currently faced by intelligent thinning technologies,including insufficient precision in young fruit recognition and positioning,significant mechanical damage to fruits,poor system stability,and high research and manufacturing costs,the paper proposed future development directions through innovative algorithms and optimized mechanical structures to facilitate technological implementation.This research aims to provide a systematic theoretical reference for the continuous development and industrial application of intelligent thinning technologies in apple orchards.
[1]国家统计局.2024中国统计年鉴[M].北京：中国统计出版社，2024.
[2]朱立成，王文贝，赵博，等.基于SDE-YOLO的矮砧密植化果园苹果检测方法[J].农业机械学报，2025，56(9)：638-647.
[3]LAMMERS K,ZHANG K,ZHU K,et al.Development and evaluation of a dual-arm robotic apple harvesting system[J].Computers and Electronics in Agriculture,2024,227:109586.
[4]周桂红，孙乐琳，梁芳芳，等.基于改进密度峰值聚类算法的梨花密度分级[J].农业工程学报，2023，39(1)：126-135.
[5]司永胜，孔德浩，王克俭，等.基于CRV-YOLO的苹果中心花和边花识别方法[J].农业机械学报，2024，55(2)：278-286.
[6]窦汉杰，陈震宇，翟长远，等.果园智能化作业装备自主导航技术研究进展[J].农业机械学报，2024，55(4)：1-22.
[7]Food and Agriculture Organization of the United Nations,FAOSTAT:crops and livestock products[EB/OL].(2025-06-11).https://www.fao.org/faostat/en/#data/QCL.
[8]刘锦月，巩铁雄，乔江波，等.近期气候变化对黄土高原苹果产区分布格局的影响[J].水土保持研究，2020，27(3)：153-158.
[9]张宏.黄土高原是我国优质苹果核心产区之我见[J].中国果树，2019(1)：114-116.
[10]王梓霖，王钊，孙亮，等.基于遥感作物参考曲线的黄土高原苹果产区始花期监测[J].遥感技术与应用，2024，39(2)：328-336.
[11]马勉娣，曾厅余，鲁兴凯，等.云南昭通与环渤海湾等3个产区红富士苹果品质的比较[J].中国果树，2024(5)：33-38.
[12]李泰，卢士军，黄家章，等.土壤养分对‘富士’苹果品质的影响——基于3省6县苹果园调查[J].中国农业大学学报，2024，29(11)：30-38.
[13]马勉娣，鲁兴凯，汪琼，等.西南冷凉高地富士苹果不同采摘期品质分析及对“糖心”形成的影响研究[J].中国南方果树，2022，51(2)：102-107.
[14]刘园，刘布春，马钧，等.西南冷凉高地苹果最大可能生育期内气候生产潜力评价——以云南昭通为例[J].中国农业气象，2021，42(2)：87-101.
[15]鲁兴凯，张秀英，张丹，等.西南冷凉高地“红富士”苹果不同采收时间和套袋对果实品质的影响[J].果树学报，2017，34(2)：196-203.
[16]邢一凡，贾一鸣，朱丽霞.不同产区红富士苹果酒品质分析[J].中国酿造，2024，43(5)：98-104.
[17]刘志杰，刘恒，毛文菊，等.面向多机器人的传统苹果园无线通信信号传播特性研究[J].农业机械学报，2022，53(8)：283-293.
[18]高芳芳，武振超，索睿，等.基于深度学习与目标跟踪的苹果检测与视频计数方法[J].农业工程学报，2021，37(21)：217-224.
[19]DU Y R,HAN Y P,SU Y Z,et al.A lightweight model based on you only look once for pomegranate before fruit thinning in complex environment[J].Engineering Applications of Artificial Intelligence,2024,137:109123.
[20]潘明，张丽慧，黄晓财，等.空天地一体化智慧果园平台设计与应用[J].现代农业装备，2021，42(4)：43-47.
[21]李媛媛，孟祥宝，钟林忆，等.基于全生长周期模型的智慧果园精准管控平台设计与实现[J].现代农业装备，2025，46(3)：38-44.
[22]SRIVASTAVA S,VANI B,SADISTAP S,et al.Handheld,smartphone based spectrometer for rapid and nondestructive testing of citrus cultivars[J].Journal of Food Measurement and Characterization,2020,15(1):892-904.
[23]HUSSAIN M,HE L,SCHUPP J,et al.Green fruit segmentation and orientation estimation for robotic green fruit thinning of apples[J].Computers and Electronics in Agriculture,2023,207:107734.
[24]GONZALEZ L,TORRES E,AVILA G,et al.Evaluation of chemical fruit thinning efficiency using Brevis (Metamitron) on apple trees ('Gala') under Spanish conditions[J].Scientia Horticulturae,2020,261:109003.
[25]卞雨观.矮砧密植苹果园分段式疏花机设计与试验研究[D].杨凌：西北农林科技大学，2023.
[26]王会征，李新龙，薄萍，等.基于改进YOLOv8n的自然场景下苹果外观品质检测方法[J].农业工程学报，2025，41(11)：173-182.
[27]李莹，刘梦莲，何自芬，等.基于改进YOLOv8s的柑橘果实成熟度检测[J].农业工程学报，2024，40(24)：157-164.
[28]吕强，林刚，蒋杰，等.基于改进YOLOv5s模型的自然场景中绿色柑橘果实检测[J].农业工程学报，2024，40(18)：147-154.
[29]师翊，王应宽，王菲，等.基于改进YOLOv8n的苹果幼果识别[J].农业工程学报，2025，41(8)：204-210.
[30]MA B,HUA Z X,WEN Y C,et al.Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments[J].Artificial Intelligence in Agriculture,2024,11:70-82.
[31]宋怀波，马宝玲，尚钰莹，等.基于YOLOv7-ECA模型的苹果幼果检测[J].农业机械学报，2023，54(6)：233-242.
[32]龙燕，杨智优，何梦菲.基于改进YOLOv7的疏果期苹果目标检测方法[J].农业工程学报，2023，39(14)：191-199.
[33]HUSSAIN M,HE L,SCHUPP J,et al.Green fruit-stem pairing and clustering for machine vision system in robotic thinning of apples[J].Journal of Field Robotics,2025,42:1463-1490.
[34]GAO Y,WANG Q Y,RAO X Q,et al.OrangeStereo:a novel orange stereo matching network for 3D surface reconstruction[J].Computers and Electronics in Agriculture,2024,217:108626.
[35]CHEN M,CHEN Z,LUO L,et al.Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard[J].Computers and Electronics in Agriculture,2024,219:108774.
[36]李志升.基于深度学习的复杂环境下苹果幼果检测与定位研究[D].青岛：青岛科技大学，2022.
[37]JANGALI R,MCGUINNESS B,LIM H,et al.Development of a novel multipurpose robotic end effector for fruitlet thinning and fruit harvesting of apples[C]//2024 IEEE 20th International Conference on Automation Science and Engineering (CASE).Bari,Italy:2024:2073-2078.
[38]HUSSAIN M,HE L,SCHUPP J,et al.Green fruit removal dynamics for development of robotic green fruit thinning end-effector[J].Journal of the ASABE,2022,65(4):779-788.
[39]PAWIKHUM K,HEINEMANN P H,HE L,et al.Design of end-effectors for apple robotic thinning in the green fruit stage[C]//2023 ASABE Annual International Meeting.St.Joseph,Michigan：American Society of Agricultural and Biological Engineers,2023:2300491.
[40]白杜娟，牛育华，白岗栓.量化修剪与喷施苹果面膜对果园经济效益的影响[J].农学学报，2023，13(8)：74-80.
[41]白杜娟，白岗栓.量化修剪对苹果园疏花疏果用工及经济效益的影响[J].中国果树，2023(7)：37-40.
[42]冯贝贝，梅闯，王磊，等.阿克苏富士苹果烟富3号化学疏花疏果的效果[J].新疆农业科学，2023，60(10)：2470-2478.
基本信息:
中图分类号:S661.1
引用信息:
[1]武振超,杨喻,石梦晴,等.传统苹果园向现代化苹果园过渡下的疏果技术研究进展[J].现代农业装备,2026,47(01):1-11.
基金信息:
国家自然科学基金青年项目(32501768); 安徽省自然科学基金青年项目(2508085QC086)
2025-12-03
2025-12-03
2025-12-03