基于改进MaskR-CNN模型的秀珍菇表型参数自动测量方法

Phenotype Analysis of Pleurotus Geesteranus Based on Improved Mask R-CNN

[目的/意义]秀珍菇表型是其品质和栽培环境适应性的反映,但目前人工测量表型参数耗时费力、主观性强,亟需自动化分析手段。[方法]一种基于改进Mask R-CNN的秀珍菇测量模型PG-Mask R-CNN (Pleurotus geesteranus-Mask Region-Based Convolutional Neural Network),提出以损伤率为指标的裂纹评价方法,并对其进行量化评价。PG-Mask R-CNN模型以Mask R-CNN为主体,通过向特征提取网络Resnet101中添加Sim AM注意力机制,在不增加原始网络参数的情况下提高网络性能;采用改进的特征金字塔进行多尺度融合,融合多层级的信息进行预测;将GIo U (Generalized Intersection over Union)边界框回归损失函数替代原有的Io U (Intersection over Union)损失函数,完善图像重叠度的计算,进一步提高模型性能。[结果和讨论] PG-Mask R-CNN模型目标检测的m AP和m AR分别为84.8%和87.7%,均高于目前主流的YOLACT (You Only Look At Coefficien Ts)、Insta Boost、Query Inst和Mask R-CNN模型;实例分割结果的MRE (Mean Relative Error)为0.90%,均低于其他实例分割模型;PG-Mask R-CNN模型的参数量为51.75 M,略大于原始的Mask R-CNN,均小于其他实例分割模型。对分割后的菌盖和裂纹进行测量,所得结果的MRE分别为1.30%和7.54%,损伤率的MAE (Mean Absolute Error)为0.14%。[结论]本研究提出的PG-Mask R-CNN模型对秀珍菇的菌柄、菌盖及裂纹识别与分割具有较高的准确率,在此基础上能够实现对秀珍菇表型参数的自动化测量,这为后续秀珍菇智慧化育种、智能栽培与分级奠定了技术基础。

[Objective]Pleurotus geesteranus is a rare edible mushroom with a fresh taste and rich nutritional elements,which is popular among consumers.It is not only cherished for its unique palate but also for its abundant nutritional elements.The phenotype of Pleurotus geesteranus is an important determinant of its overall quality,a specific expression of its intrinsic characteristics and its adaptation to various cultivated environments.It is crucial to select varieties with excellent shape,integrity,and resistance to cracking in the breeding process.However,there is still a lack of automated methods to measure these phenotype parameters.The method of manual measurement is not only time-consuming and labor-intensive but also subjective,which lead to inconsistent and inaccurate results.Thus,the traditional approach is unable to meet the demand of the rapid development Pleurotus geesteranus industry.[Methods]To solve the problems which mentioned above,firstly,this study utilized an industrial-grade camera(Daheng MER-500-14GM)and a commonly available smartphone(Redmi K40)to capture high-resolution images in DongSheng mushroom industry(Jiujiang,Jiangxi province).After discarding blurred and repetitive images,a total of 344 images were collected,which included two commonly distinct varieties,specifically Taixiu 57 and Gaoyou 818.A series of data augmentation algorithms,including rotation,flipping,mirroring,and blurring,were employed to construct a comprehensive Pleurotus geesteranus image dataset.At the end,the dataset consisted of 3440 images and provided a robust foundation for the proposed phenotype recognition model.All images were divided into training and testing sets at a ratio of 8:2,ensuring a balanced distribution for effective model training.In the second part,based upon foundational structure of classical Mask R-CNN,an enhanced version specifically tailored for Pleurotus geesteranus phenotype recognition,aptly named PG-Mask R-CNN(Pleurotus geesteranus-Mask Region-based Convolutional Neural Network)was designed.The PG-Mask R-CNN network was refined through three approaches:(1)To take advantage of the attention mechanism,the SimAM attention mechanism was integrated into the third layer of ResNet101feature extraction network after analyzing and comparing carefully,it was possible to enhance the network's performance without increasing the original network parameters.(2)In order to avoid the problem of Mask R-CNN's feature pyramid path too long to split low-level feature and high-level feature,which may impair the semantic information of the high-level feature and lose the positioning information of the low-level feature,an improved feature pyramid network was used for multiscale fusion,which allowed us to amalgamate information from multiple levels for prediction.(3)To address the limitation of IoU(Intersection over Union)bounding box,which only considered the overlapping area between the prediction box and target box while ignoring the non-overlapping area,a more advanced loss function called GIoU(Generalized Intersection over Union)was introduced.This replacement improved the calculation of image overlap and enhanced the performance of the model.Furthermore,to evaluate crack state of Pleurotus geesteranus more scientifically,reasonably and accurately,the damage rate as a new crack quantification evaluation method was introduced,which was calculated by using the proportion of cracks in the complete pileus of the mushroom and utilized the MRE(Mean Relative Error)to calculate the mean relative error of the Pleurotus geesteranus's damage rate.Thirdly,the PG-Mask R-CNN network was trained and tested based on the Pleurotus geesteranus image dataset.According to the detection and segmentation results,the measurement and accuracy verification were conducted.Finally,considering that it was difficult to determine the ground true of the different shapes of Pleurotus geesteranus,the same method was used to test 4 standard blocks of different specifications,and the rationality of the proposed method was verified.[Results and Discussions]In the comparative analysis,the PG-Mask R-CNN model was superior to Grabcut algorithm and other 4 instance segmentation models,including YOLACT(You Only Look At Coefficien Ts),InstaBoost,QueryInst,and Mask R-CNN.In object detection tasks,the experimental results showed that PG-Mask R-CNN model achieved a mAP of 84.8%and a mAR(mean Average Recall)of 87.7%,respectively,higher than the five methods were mentioned above.Furthermore,the MRE of the instance segmentation results was 0.90%,which was consistently lower than that of other instance segmentation models.In addition,from a model size perspective,the PG-Mask R-CNN model had a parameter count of 51.75 M,which was slightly larger than that of the unimproved Mask R-CNN model but smaller than other instance segmentation models.With the instance segmentation results on the pileus and crack,the MRE were 1.30%and 7.54%,respectively,while the MAE of the measured damage rate was 0.14%.[Conclusions]The proposed PG-Mask R-CNN model demonstrates a high accuracy in identifying and segmenting the stipe,pileus,and cracks of Pleurotus geesteranus.Thus,it can help the automated measurements of phenotype measurements of Pleurotus geesteranus,which lays a technical foundation for subsequent intelligent breeding,smart cultivation and grading of Pleurotus geesteranus.