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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 61-74.doi: 10.12133/j.smartag.2020.2.3.202007-SA002

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利用图像和机器学习检测大豆作物幼苗期玉米杂苗

FLORESPaulo1(), MATHEWJithin2, JAHANNusrat1, STENGERJohn1   

  1. 1.北达科他州州立大学 农业与生物工程系,北达科他州法戈市 58102,美国
    2.北达科他州州立大学 植物科学系,北达科他州法戈市 58102,美国
  • 收稿日期:2020-07-01 修回日期:2020-08-01 出版日期:2020-09-30 发布日期:2020-12-09

Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning

FLORES Paulo1(), ZHANG Zhao1(), MATHEW Jithin2, JAHAN Nusrat1, STENGER John1   

  1. 1.Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
    2.Department of Plant Sciences, North Dakota State University, Fargo, ND 58108, USA
  • Received:2020-07-01 Revised:2020-08-01 Online:2020-09-30 Published:2020-12-09
  • corresponding author: Zhao ZHANG E-mail:paulo.flores@ndsu.edu;zhao.zhang.1@ndsu.edu
  • About author:Paulo Flores (1979-), male, assistant professor, research interests: precision agriculture, remote sensing. E-mail: paulo.flores@ndsu.edu.
  • Supported by:
    NDSU-AES Project(FARG005348)

摘要:

在大豆-玉米轮作生产过程中,玉米杂苗会与大豆苗竞争水和肥料,而且很容易遮住大豆苗,影响害虫(如玉米根虫)的防控,降低大豆品质。因此,在大豆幼苗期及时检测出玉米杂苗并对其进行处理非常重要。传统的人工检测方法主观性强、效率低,传感器和算法的发展为自动检测玉米杂苗提供了更好的解决方案。本研究在温室环境下模仿田间条件,待玉米和大豆发芽后,连续5天用因特尔RealSense D435相机采集彩色图像,并人工裁剪幼苗图像区域,在此基础上对图像进行分割和去噪。在采集图像形状、色彩和纹理特征值后, 对所采集的特征值进行权重分析, 保留前10种重要的特征值导入基于特征的机器学习算法中进行模型训练和预测。预测结果表明,支持向量机模型(SVM)、神经网络(NN)和随机森林(RF)的预测精度分别为85.3%,81.5%和82.6%。将数据集导入GoogLeNet和VGG-16 两种深度学习模型进行训练, 预测精度分别为96.0%和96.2%。VGG-16 模型在区分大豆幼苗和玉米杂苗中有较好的表现,彩色图像和VGG-16 模型组成的系统可以自动检测大豆生长过程中玉米杂苗的情况,为农民提供准确的信息,帮助其进行生产决策和田间管理。

关键词: 玉米-大豆轮作, 玉米杂苗, 图像处理, 机器学习, 深度学习, 支持向量机(SVM)

Abstract:

Volunteer corn in soybean fields are harmful as they disrupt the benefits of corn-soybean rotation. Volunteer corn does not only reduce soybean yield by competing for water, nutrition and sunlight, but also interferes with pest control (e.g., corn rootworm). It is therefore critical to monitor the volunteer corn in soybean at the crop seedling stage for better management. The current visual monitoring method is subjective and inefficient. Technology progress in sensing and automation provides a potential solution towards the automatic detection of volunteer corn from soybean. In this study, corn and soybean were planted in pots in greenhouse to mimic field conditions. Color images were collected by using a low-cost Intel RealSense camera for five successive days after the germination. Individual crops from images were manually cropped and subjected to image segmentation based on color threshold coupled with noise removal to create a dataset. Shape (i.e., area, aspect ratio, rectangularity, circularity, and eccentricity), color (i.e., R, G, B, H, S, V, L, a, b, Y, Cb, and Cr) and texture (coarseness, contrast, linelikeness, and directionality) features of individual crops were extracted. Individual feature's weights were ranked with the top 12 relevant features selected for this study. The 12 features were fed into three feature-based machine learning algorithms: support vector machine (SVM), neural network (NN) and random forest (RF) for model training. Prediction precision values on the test dataset for SVM, NN and RF were 85.3%, 81.6%, and 82.0%, respectively. The dataset (without feature extraction) was fed into two deep learning algorithms—GoogLeNet and VGG-16, resulting into 96.0% and 96.2% accuracies, respectively. The more satisfactory models from feature-based machine learning and deep learning were compared. VGG-16 was recommended for the purpose of distinguishing volunteer corn from soybean due to its higher detection accuracy, as well as smaller standard deviation (STD). This research demonstrated RGB images, coupled with VGG-16 algorithm could be used as a novel, reliable (accuracy >96%), and simple tool to detect volunteer corn from soybean. The research outcome helps provide critical information for farmers, agronomists, and plant scientists in monitoring volunteer corn infestation conditions in soybean for better decision making and management.

Key words: corn and soybean rotation, volunteer corn, image processing, machine learning, deep learning, support vector machine (SVM)CLC number: S52, TP181 Documents code: A Article ID:202007-SA002