智慧农业（中英文）2024，Vol.6Issue(2)：95-106,12.DOI:10.12133/j.smartag.SA202310014
基于实例分割技术的草莓叶龄及冠幅表型快速提取方法
Fast Extracting Method for Strawberry Leaf Age and Canopy Width Based on Instance Segmentation Technology
摘要
Abstract
[Objective]There's a growing demand among plant cultivators and breeders for efficient methods to acquire plant phenotypic traits at high throughput,facilitating the establishment of mappings from phenotypes to genotypes.By integrating mobile phenotyping plat-forms with improved instance segmentation techniques,researchers have achieved a significant advancement in the automation and ac-curacy of phenotypic data extraction.Addressing the need for rapid extraction of leaf age and canopy width phenotypes in strawberry plants cultivated in controlled environments,this study introduces a novel high-throughput phenotyping extraction approach leverag-ing a mobile phenotyping platform and instance segmentation technology. [Methods]Data acquisition was conducted using a compact mobile phenotyping platform equipped with an array of sensors,including an RGB sensor,and edge control computers,capable of capturing overhead images of potted strawberry plants in greenhouses.Target-ed adjustments to the network structure were made to develop an enhanced convolutional neural network(Mask R-CNN)model for processing strawberry plant image data and rapidly extracting plant phenotypic information.The model initially employed a split-at-tention networks(ResNeSt)backbone with a group attention module,replacing the original network to improve the precision and effi-ciency of image feature extraction.During training,the model adopted the Mosaic method,suitable for instance segmentation data augmentation,to expand the dataset of strawberry images.Additionally,it optimized the original cross-entropy classification loss func-tion with a binary cross-entropy loss function to achieve better detection accuracy of plants and leaves.Based on this,the improved Mask R-CNN description involves post-processing of training results.It utilized the positional relationship between leaf and plant masks to statistically count the number of leaves.Additionally,it employed segmentation masks and image calibration against true val-ues to calculate the canopy width of the plant. [Results and Discussions]This research conducted a thorough evaluation and comparison of the performance of an improved Mask R-CNN model,underpinned by the ResNeSt-101 backbone network.This model achieved a commendable mask accuracy of 80.1%and a detection box accuracy of 89.6%.It demonstrated the ability to efficiently estimate the age of strawberry leaves,demonstrating a high plant detection rate of 99.3%and a leaf count accuracy of 98.0%.This accuracy marked a significant improvement over the origi-nal Mask R-CNN model and meeting the precise needs for phenotypic data extraction.The method displayed notable accuracy in mea-suring the canopy widths of strawberry plants,with errors falling below 5%in about 98.1%of cases,highlighting its effectiveness in phenotypic dimension evaluation.Moreover,the model operated at a speed of 12.9 frames per second(FPS)on edge devices,effec-tively balancing accuracy and operational efficiency.This speed proved adequate for real-time applications,enabling rapid phenotypic data extraction even on devices with limited computational capabilitie. [Conclusions]This study successfully deployed a mobile phenotyping platform combined with instance segmentation techniques to an-alyze image data and extract various phenotypic indicators of strawberry plant.Notably,the method demonstrates remarkable robust-ness.The seamless fusion of mobile platforms and advanced image processing methods not only enhances efficiency but also ignifies a shift towards data-driven decision-making in agriculture.关键词
移动式表型平台/实例分割/草莓表型/叶龄统计/冠幅/Mask R-CNN/ResNeStKey words
mobile phenotype platform/instance segmentation/strawberry plant phenotype/leaf age/plant crown width/Mask R-CNN/ResNeSt分类
信息技术与安全科学引用本文复制引用
樊江川,王源桥,苟文博,蔡双泽,郭新宇,赵春江..基于实例分割技术的草莓叶龄及冠幅表型快速提取方法[J].智慧农业（中英文）,2024,6(2):95-106,12.基金项目
北京市科技新星计划(Z211100002121065) （Z211100002121065）
北京市科技新星计划交叉合作课题(Z20220484202) （Z20220484202）
"十四五"国家重点研发计划项目(2022YFD2002302-02) Beijing Nova Program(Z211100002121065,Z20220484202) （2022YFD2002302-02）
National Key R&D Program(2022YFD2002302-02) （2022YFD2002302-02）
