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United States Department of Agriculture

Agricultural Research Service

Research Project: Pesticide Application Technologies for Spray-drift Management, Maximizing In-field Deposition, and Targeted Spraying

Location: Crop Production Systems Research Unit

Title: Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

Authors
item Deng, Wei -
item Huang, Yanbo
item Zhao, Chunjiang -
item Chen, Liping -
item Meng, Zhijun -

Submitted to: International Agricultural Engineering Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 20, 2011
Publication Date: July 1, 2011
Citation: Deng, W., Huang, Y., Zhao, C., Chen, L., Meng, Z. 2011. Comparison of SVM, RBF-NN, and DT for crop and weed identification based on spectral measurement over corn fields. International Agricultural Engineering Journal. 20(1):11-19.

Interpretive Summary: The classification algorithms of soft computing have a great potential to be developed for identification of crop and weeds accurately in fields. In this study, three advanced computing methods have been evaluated for crop and weeds classification based on spectral measurement on plants in corn fields. The method of Support Vector Machine (SVM), a new soft computing technique, was evaluated and compared with two other methods, Decision Tree (DT), and Radial Basis Function Neural Network (RBF-NN) in data classification. The purpose of the classification work was to identify seedling corns, and two kinds of weeds, Dchinochloa crasgalli, and Echinochloa crusgalli weeds in fields. The results showed that the SVM method provided an 81.58% correct classification rate of the corn and two weeds while the methods of DT and RBF-NN provided the rates of 63.16% and 52.63%, respectively. This illustrates that among the three methods, SVM has the highest accuracy in identification of corn and weeds in the fields, and SVM could be used to build a real time tool to identify crop and weeds accurately in practice.

Technical Abstract: It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision Tree (DT), and Radial Basis Function Neural Network (RBF-NN). The three methods were used to model and classify the spectral data measured on plants in the corn fields. The spectral measurement was conducted over the fields covered by seedling corns, and two kinds of weeds, Dchinochloa crasgalli, and Echinochloa crusgalli weeds. Among the measured data, respectively 48 corn samples (category 1), 33 Echinochloa crusgalli samples (category 2), and 33 Dchinochloa crasgalli samples (category 3) were randomly selected as the sample data. The sample data were divided into a training data set (2/3 of the data) and a testing data set (1/3 of the data). The SVM method provided an 81.58% correct classification rate of the corn and two weeds with the testing data set while the methods of DT and RBF-NN provided the rates of 63.16% and 52.63%, respectively. This indicates that among the three methods, SVM has the highest accuracy in identification of corn and weeds in the fields in the case of limited samples. SVM could provide a method to build a real time tool to identify crop and weeds with high accuracy in practice.

Last Modified: 4/19/2014
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