Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning

Yali Zhang, Luchao Bai, Yuan Qi, Huasheng Huang, Xiaoyang Lu, Junqi Xiao, Yubin Lan, Muhua Lin and Jizhong Deng
Additional contact information
Yali Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Luchao Bai: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yuan Qi: College of Mechanical Engineering, Nantong Vocational University, Nantong 226007, China
Huasheng Huang: College of Computer Sciences, Guangdong Polytechnic Normal University, Zhongshan Road, Guangzhou 510642, China
Xiaoyang Lu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Junqi Xiao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yubin Lan: National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Muhua Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jizhong Deng: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2022, vol. 12, issue 6, 1-17

Abstract: Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec ® HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice.

Keywords: hyperspectral; machine learning; CNN; rice spikelets; RF; SVM; BP neural network; PCA; GA analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/6/755/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/6/755/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:6:p:755-:d:824293

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().