摘要
【目的】提升柑橘果园的智能化管理水平,快速无损获取柑橘树冠层的施药情况,改善小规模数据集导致施药情况分类模型易发生过拟合的问题。【方法】提出一种基于卷积神经网络的柑橘树冠层施药情况分类模型-VGG_C模型。模型以VGG模型核心思想为基础进行构建,通过交叉熵损失函数优化,加速概率分布与真实分布的迭代过程,并在输出端引入不确定性度量计算以及在下采样模块中插入Droupout方法,降低由于数据较少而发生过拟合的概率。【结果】VGG_C模型针对训练集的分类损失值为0.44%,比ResNet和VGG模型分别降低了87%和91%;准确率为95.3%,比ResNet和VGG模型分别提高了5%和10%;验证集的预测平均准确率为96.4%。【结论】VGG_C模型通过多层卷积模型协同实现柑橘冠层热红外图像特征的高效提取,通过优化输出端结构提高了柑橘冠层施药情况分类模型在小数据集规模上的训练测试优度,可为柑橘树施药情况的智能化判断提供有效参考。
【Objective】The study was aimed to improve the intelligent management level of citrus orchards,quickly and non-destructively evaluate the spraying quality on citrus canopy,and solve the overfitting problem of the spraying quality classification model caused by small-scale data set.【Method】We proposed a classification model of spraying quality on citrus canopy based on convolutional neural network:Visual geometry group citrus model(VGG_C model).The model was constructed based on the core idea of the VGG model.Through optimization of the cross-entropy loss function,the iterative process of probability distribution and true distribution was accelerated.The uncertainty measurement calculation was introduced at the output end and the Droupout method was inserted in the downsampling module to reduce the probability of overfitting due to small amount of data.【Result】The loss value of VGG_C model for the training set was 0.44%,which was 87%and 91%lower than that of ResNet and VGG respectively.The accuracy of VGG_C model for the training set was 95.3%,which was 5%and 10%higher than that of ResNet and VGG respectively.The average accuracy of the verification set was 96.4%.【Conclusion】VGG_C model can effectively extract the features of citrus canopy thermal infrared image through multi-layer convolution model and improve the training and testing superiority of citrus canopy application classification model in small data set by optimizing the output structure.VGG_C model can provide an effective reference for the intelligent judgment of pesticide application on citrus trees.
作者
徐相华
林佳翰
陆健强
陈宏泽
杨瑞帆
XU Xianghua;LIN Jiahan;LU Jianqiang;CHEN Hongze;YANG Ruifan(Engineering Fundamental Teaching and Training Center,South China Agricultural University,Guangzhou 510642,China;College of Electronic Engineering/College of Artificial Intelligence,South China Agricultural University,Guangzhou 510642,China;National Precision Agriculture International Joint Research Center of Aerial Application Technology,Guangzhou 510642,China)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2021年第5期127-132,共6页
Journal of South China Agricultural University
基金
广东省重点领域研发计划(2019B020214003)
广东省普通高校“人工智能”重点领域专项(2019KZDZX1012)。
关键词
柑橘
喷施药剂
红外热感图像
卷积神经网络
交叉熵损失函数
citrus
pesticide spraying
infrared thermal image
convolutional neural network
cross entropy error function
