Vegetable Recognition Based on Unmanned Aerial Vehicle （UAV） Multispectral Imagery and Random Forest Algorithm

doi:10.13304/j.nykjdb.2022.0027

Abstract

Abstract:

Real-time and accurate information about vegetable distribution is critical for improving the management of water/fertilizer and accurate estimation of the yield. Based on spectrum features of the segmented UAV multispectral images objects， other features including index， textural and geometric features were added to build eight classification schemes （S1～S8）， and then the random forest algorithm was used to classify and analyze the classification effect. The results showed that the scheme S5 （SPEC+GLCM+INDE） performed better than the others， with overall accuracy and Kappa of 92.75% and 0.92， respectively. We discovered that adding geometric features reduced the classification accuracy， while adding index and texture features had the opposite effect. It was difficult to distinguish the Chinese cabbage and cabbage effectively only by spectrum， index and texture features， so it was necessary to introduce features such as height to improve the accuracy in the follow-up research. The feature importance ranking results showed that spectrum features， followed by the index features， had the greatest impact on classification accuracy. To summarize， the method proposed in this study， which combined object-oriented and RF algorithm， could achieve higher classification accuracy based on multi-spectral images from UAV， and the important features that affect vegetable classification could be effectively identified， providing a reference for the accurate identification of other crops.

Key words: unmanned aerial vehicle, random forests, vegetable, multi-spectral image, object-oriented method

摘要：

实时准确的蔬菜种植信息是实现水肥精准管理和产量准确估算的重要基础。对无人机多光谱影像进行分割，以光谱特征（spectrum features，SPEC）为基础，分别引入指数特征（index features，INDE）、纹理特征（grey-level co-occurrence matrix features，GLCM）和几何特征（geometric features，GEOM）构建8个分类方案（S1～S8），使用随机森林算法进行分类并分析分类效果。结果表明，方案S5（SPEC+GLCM+INDE）的分类效果最好，总体精度和Kappa系数分别为92.75%和0.92。几何特征的引入降低了分类精度，而纹理和指数特征则与其相反；仅依靠光谱、指数和纹理特征仍难以有效区分白菜和包菜，为提高精度后续研究有必要引入植株高度等特征；在4大类特征中，重要性排在首位的是光谱特征，其次为指数特征。基于无人机多光谱影像和随机森林算法能获得较高的蔬菜分类精度，并能确认影响精度的重要特征，可为其他作物的精准识别提供借鉴。

关键词: 无人机, 随机森林, 蔬菜, 多光谱影像, 面向对象

CLC Number:

S127

Qian GUO, Jiahao WEI, Jian ZHANG, Zhangxi YE, Houxi ZHANG, Zhengqing LAI, Hui DENG. Vegetable Recognition Based on Unmanned Aerial Vehicle （UAV） Multispectral Imagery and Random Forest Algorithm[J]. Journal of Agricultural Science and Technology, 2023, 25(2): 99-110.

郭倩, 魏嘉豪, 张健, 叶章熙, 张厚喜, 赖正清, 邓辉. 基于无人机多光谱影像和随机森林的蔬菜识别[J]. 中国农业科技导报, 2023, 25(2): 99-110.

Figures/Tables 12

Fig. 1 RGB image of research area

Fig. 2 Technical flow

Table 1 Different classification schemes

方案 Scheme	子特征集 Feature subset	特征总数 Number of total features
S1	SPEC	12
S2	SPEC+GLCM	52
S3	SPEC+INDE	38
S4	SPEC+GEOM	30
S5	SPEC+GLCM+INDE	78
S6	SPEC+GLCM+GEOM	70
S7	SPEC+INDE+GEOM	56
S8	SPEC+GLCM+INDE+GEOM	96

Table 2 Samples for different categories

地物类型 Type of object	总样本数 Total samples
番薯 Sweet potato	150
葱 Shallot	60
油麦菜 Leaf lettuce	100
生菜 Lettuce	110
包菜 Cabbage	100
花椰菜 Cauliflower	100
芥菜 Mustard	70
白菜 Chinese cabbage	80
水 Water	60
土壤 Soil	150
地膜 Mulch film	70

Fig. 3 Object spectrum image

Fig. 4 Segmentation effect of different segmentation scales

Fig. 5 Out-of-bag error rate of different numbers of features and different numbers of decision trees for various schemes

Fig. 6 Classification accuracy of different schemesNote： × in the figure shows the location of the mean.

Fig. 7 Producer accuracy of different schemes for different vegetable categories

Fig. 8 User accuracy of different schemes for different vegetable categories

Fig. 9 The twenty most important features for different models

Fig. 10 Classification result based on the best scheme

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