基于卷积神经网络的农作物植株干旱检测  

Drought detection of crop plants based on convolutional neural network

作　　者：吴建[1] 王红军[2] WU Jian;WANG Hongjun(Guang’an Vocational and Technology College,Guang’an 638000,China;Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]广安职业技术学院,四川广安638000 [2]西南交通大学,四川成都611756

出　　处：《中国测试》2022年第4期102-109,共8页China Measurement & Test

基　　金：国家自然科学基金(61773324,61603313)。

摘　　要：近年来,随着计算机视觉技术的快速发展,越来越多基于图像的应用被开发出来用以解决农业生产中的问题。目前对农作物生长和产量影响最大的就是植物干旱胁迫问题。传统卷积神经网(CNN)使用直接回归玉米叶片数的算法,在实际实验中误差较大,因此该文引入CNN和Fisher向量编码联合提取多尺度特征的方法。在设计特征提取网络时参考GoogLeNet中多尺度卷积核结构,使其更适合于提取不同尺寸叶片的特征。然后,利用Fisher向量对部分中间层特征图编码,增强特征的表达能力。最后,使用随机森林回归叶片数量,并在实验中展示不同干旱程度对玉米叶片数量的影响:在一定条件下,土壤含水量与叶片数量呈正相关。该文方法与现有算法进行对比,叶片平均误差以及均方误差分别减少0.011与0.382,具有一定的研究价值。In recent years,with the rapid development of computer vision technology,more and more imagebased applications have been developed to solve the problems in agricultural production.At present,plant drought stress is the most important factor affecting crop growth and yield.The traditional convolutional neural network uses the direct regression algorithm to count maize leaf number,which has a large error in the actual experiment.Therefore,this paper introduces the method of CNN and Fisher vector coding to extract multiscale features.In the design of feature extraction network,we refer to the multi-scale convolution core structure of Google LeNet to make it more suitable for extracting the characteristics of blades of different sizes.Then,Fisher vectors are used to encode the middle-level feature maps to enhance the ability of feature expression.Finally,the number of leaves was regressed by random forest,and the effects of different drought degrees on the number of maize leaves were demonstrated in the experiment.Under certain conditions,The soil moisture content is positively correlated with the number of leaves.The method in this paper is compared with the existing algorithms,and the average error and mean square error of the leaves are reduced by 0.011 and 0.382 respectively,which has certain research value.

关 键 词：玉米干旱检测 计算机视觉 CNN Fisher向量 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]