基于机器视觉与机器学习的火龙果重量估计  

Dragon fruit weight estimation based on machine vision and machine learning

作　　者：梁英凯 商枫楠 陈桥 肖明玮 罗陈迪 李文涛[1,2,3] 周学成 LIANG Ying-kai;SHANG Feng-nan;CHEN Qiao;XIAO Ming-wei;LUO Chen-di;LI Wen-tao;ZHOU Xue-cheng(College of Engineering,South China Agricultural University,Guangzhou,Guangdong 510642,China;Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence,Guangzhou,Guangdong 510642,China;Key Laboratory of Key Technology on Agricultural Machine and Equipment,Ministry of Education,Guangzhou,Guangdong 510642,China)

机构地区：[1]华南农业大学工程学院,广东广州510642 [2]广东省农业人工智能重点实验室,广东广州510642 [3]南方农业机械与装备关键技术教育部重点实验室,广东广州510642

出　　处：《食品与机械》2023年第7期99-103,共5页Food and Machinery

基　　金：国家重点研发计划项目(编号:2017YFD0700602)。

摘　　要：目的:解决火龙果人工称重耗时、费力且成本高昂等问题,提出一种基于机器视觉和机器学习的自动化重量估计方法。方法:首先,对106个火龙果进行称重、记录重量并拍摄、构建火龙果图像。然后,对火龙果进行降噪和分割,得到火龙果的二值图像,并从中提取出火龙果像素面积、长轴像素长度和短轴像素长度3项图像特征。将以上图像特征与重量组合成数据集,按照7∶3比例将数据集划分为训练集和测试集。最后,将训练集输入梯度提升、随机森林、K近邻和人工神经机器模型中训练,并利用测试集进行模型评估。结果:人工神经网络评价指标相较于其他模型更优,决定系数为0.986,均方根误差为13.091。结论:该方法能够有效地完成火龙果重量估计,满足火龙果重量估计的要求。Objective:In order to solve the problem of manual weighting of dragon fruit,including time-consuming,laborious and expensive,an automated weight estimation method based on machine vision and machine learning was proposed in this research.Methods:Firstly,106 dragon fruits were weighed,recorded and photographed,and images of dragon fruits were constructed.Secondly,binary images were obtained after denoising and segmentation.Moreover,the three features of pixel area,major axis pixel length and minor axis pixel length of dragon fruits were extracted on the basis of binary images.The three features of each image and their corresponding weights were combined into a set of data,which was divided into training set and test set according to the ratio of 7∶3.Finally,the training set was input into the Gradient Boosting,Random Forest,K-Neighbors and Artificial Neural Networks machine-learning models for training,and the test sets were used for model evaluation.Results:The evaluation index of the Artificial Neural network performed well compared with other models,with R^(2) of 0.986 and RMSE of 13.091.Conclusion:The experimental result demonstrates that the method proposed in this research can accomplish the weight estimation of dragon fruit effectively,and meet the weight estimation requirements of dragon fruit.

关 键 词：机器视觉 机器学习 火龙果 重量估计 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] S667.9[农业科学—果树学]