Spectroscopy and Spectral Analysis, Volume. 42, Issue 9, 2969(2022)
Hyperspectral Latent Period Diagnosis of Tomato Gray Mold Based on TLBO-ELM Model
Figures & Tables(10)
Fig. 2. Flowchart of tomato gray mold diagnosis and classification
Fig. 3. Hyperspectral images of inoculated pathogenic sample #1 in the first 8 days
Fig. 4. Hyperspectral mean reflectivity curves of tomato leaves in different periods
Fig. 6. Feature wavebands extracted by DWT-CARS algorithm for three times
(a): The first extraction; (b): The second extraction; (c): The third extraction
Fig. 7. Feature bands extracted by DWT-CARS algorithm for three times
(a): The first extraction; (b): The second extraction; (c): The third extraction
Fig. 8. Confusion matrix of three model test sets
(a): DWT-FC-TLBO-ELM; (b): DWT-TLBO-ELM; (c): DWT-CARS-TLBO-ELM
Table 1. Feature bands extracted by DWT-CARS algorithm for three times
View tableView in ArticleTable 1. Feature bands extracted by DWT-CARS algorithm for three times
DWT-CARS方法 第1次 第2次 第3次 特征波段个数/个 5 5 7 特征波段/nm 696, 765, 769, 838, 840 696, 767, 769, 772, 840 694, 696, 767, 769, 778, 838, 840 RMSECV/% 0.295 9 0.294 5 0.297 4 最终特征波段/nm 694, 696, 765, 767, 769, 772, 778, 838, 840
Table 2. Model parameters and test set accuracy, accuracy, recall rate and
F 1 valueView tableView in ArticleTable 2. Model parameters and test set accuracy, accuracy, recall rate and
F 1 value诊断模型 模型参数 测试集
准确率/%测试集
精确率/%召回率
/%F1
/%最优权值ω 隐藏层最优阈值b DWT-FC-TLBO-ELM 1.044 8 155 95.71 96.47 95.71 96.09 DWT-TLBO-ELM 1.044 8 70 95.71 96.17 95.71 95.94 DWT-CARS-TLBO-ELM 1 107 100 100 100 100
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. Hyperspectral Latent Period Diagnosis of Tomato Gray Mold Based on TLBO-ELM Model[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2969
Paper Information
Category: Research Articles
Received: Jul. 19, 2021
Accepted: Oct. 12, 2021
Published Online: Nov. 17, 2022
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