Optimizing maize germination forecasts with random forest and data fusion techniques
Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2468.pdf |
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| author | Lili Wu Yuqing Xing Kaiwen Yang Wenqiang Li Guangyue Ren Debang Zhang Huiping Fan |
| author_facet | Lili Wu Yuqing Xing Kaiwen Yang Wenqiang Li Guangyue Ren Debang Zhang Huiping Fan |
| author_sort | Lili Wu |
| collection | DOAJ |
| description | Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a flat capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates. |
| format | Article |
| id | doaj-art-1e01a23a8b394741ace5915be73fcedc |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-1e01a23a8b394741ace5915be73fcedc2024-11-30T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e246810.7717/peerj-cs.2468Optimizing maize germination forecasts with random forest and data fusion techniquesLili Wu0Yuqing Xing1Kaiwen Yang2Wenqiang Li3Guangyue Ren4Debang Zhang5Huiping Fan6College of Sciences, Henan Agricultural University, Zhengzhou, ChinaCollege of Food and Bioengineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Sciences, Henan Agricultural University, Zhengzhou, ChinaCollege of Sciences, Henan Agricultural University, Zhengzhou, ChinaCollege of Food and Bioengineering, Henan University of Science and Technology, Luoyang, ChinaZhengzhou Wangu Machinery Co., Ltd, Zhengzhou, ChinaCollege of Food Science and Technology, Henan Agricultural University, Zhengzhou, ChinaTraditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a flat capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates.https://peerj.com/articles/cs-2468.pdfMaize seedsGermination rateNon-destructive predictionRandom forest algorithmImage processing |
| spellingShingle | Lili Wu Yuqing Xing Kaiwen Yang Wenqiang Li Guangyue Ren Debang Zhang Huiping Fan Optimizing maize germination forecasts with random forest and data fusion techniques PeerJ Computer Science Maize seeds Germination rate Non-destructive prediction Random forest algorithm Image processing |
| title | Optimizing maize germination forecasts with random forest and data fusion techniques |
| title_full | Optimizing maize germination forecasts with random forest and data fusion techniques |
| title_fullStr | Optimizing maize germination forecasts with random forest and data fusion techniques |
| title_full_unstemmed | Optimizing maize germination forecasts with random forest and data fusion techniques |
| title_short | Optimizing maize germination forecasts with random forest and data fusion techniques |
| title_sort | optimizing maize germination forecasts with random forest and data fusion techniques |
| topic | Maize seeds Germination rate Non-destructive prediction Random forest algorithm Image processing |
| url | https://peerj.com/articles/cs-2468.pdf |
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