Feature Recognition of Crop Growth Information in Precision Farming
To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavele...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9250832 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550302073487360 |
|---|---|
| author | Hanqing Sun Xiaohui Zhang Zhou Yu Gang Xi |
| author_facet | Hanqing Sun Xiaohui Zhang Zhou Yu Gang Xi |
| author_sort | Hanqing Sun |
| collection | DOAJ |
| description | To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals. |
| format | Article |
| id | doaj-art-bd526a324b1248378c4cb00cff40f4f2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-bd526a324b1248378c4cb00cff40f4f22025-02-03T06:07:11ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/92508329250832Feature Recognition of Crop Growth Information in Precision FarmingHanqing Sun0Xiaohui Zhang1Zhou Yu2Gang Xi3Department of Electronic and Information Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou 450044, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Information Engineer, Henan Institute of Science and Technology, Xinxiang 453003, ChinaDepartment of Applied Physics, Xi’an University of Technology, Xi’an 710048, ChinaTo identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.http://dx.doi.org/10.1155/2018/9250832 |
| spellingShingle | Hanqing Sun Xiaohui Zhang Zhou Yu Gang Xi Feature Recognition of Crop Growth Information in Precision Farming Complexity |
| title | Feature Recognition of Crop Growth Information in Precision Farming |
| title_full | Feature Recognition of Crop Growth Information in Precision Farming |
| title_fullStr | Feature Recognition of Crop Growth Information in Precision Farming |
| title_full_unstemmed | Feature Recognition of Crop Growth Information in Precision Farming |
| title_short | Feature Recognition of Crop Growth Information in Precision Farming |
| title_sort | feature recognition of crop growth information in precision farming |
| url | http://dx.doi.org/10.1155/2018/9250832 |
| work_keys_str_mv | AT hanqingsun featurerecognitionofcropgrowthinformationinprecisionfarming AT xiaohuizhang featurerecognitionofcropgrowthinformationinprecisionfarming AT zhouyu featurerecognitionofcropgrowthinformationinprecisionfarming AT gangxi featurerecognitionofcropgrowthinformationinprecisionfarming |