Predicting Key Grassland Characteristics from Hyperspectral Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Design
2.2. Sensor Rig
2.3. Measurement of Grass Qualities
2.4. Model Generation
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature | Wavelengths in Nanometres | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Biomass | 450 | 490 | 519 | 544 | 587 | 591 | 647 | 653 | 664 | 698 | 806 | 1020 |
Dry% | 398 | 413 | 431 | 555 | 608 | 629 | 660 | 698 | 860 | 881 | 902 | 962 |
Sugars | 394 | 512 | 541 | 601 | 619 | 657 | 677 | 761 | 816 | 819 | 916 | 1015 |
Nitrates | 394 | 548 | 615 | 629 | 677 | 691 | 728 | 731 | 761 | 809 | 819 | 993 |
10-Fold Cross Validation | Biomass | Dry% | Sugars | Nitrates |
---|---|---|---|---|
Model1 Coefficient of Determination | 0.624 | 0.514 | 0.550 | 0.489 |
Model2 Coefficient of Determination | 0.607 | 0.522 | 0.548 | 0.494 |
Model3 Coefficient of Determination | 0.628 | 0.548 | 0.498 | 0.498 |
Model4 Coefficient of Determination | 0.614 | 0.508 | 0.476 | 0.476 |
Model5 Coefficient of Determination | 0.622 | 0.538 | 0.471 | 0.471 |
Average Coefficient of Determination | 0.62 | 0.53 | 0.54 | 0.49 |
Average RMSEP/σ | 0.61 | 0.68 | 0.67 | 0.72 |
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Jackman, P.; Lee, T.; French, M.; Sasikumar, J.; O’Byrne, P.; Berry, D.; Lacey, A.; Ross, R. Predicting Key Grassland Characteristics from Hyperspectral Data. AgriEngineering 2021, 3, 313-322. https://doi.org/10.3390/agriengineering3020021
Jackman P, Lee T, French M, Sasikumar J, O’Byrne P, Berry D, Lacey A, Ross R. Predicting Key Grassland Characteristics from Hyperspectral Data. AgriEngineering. 2021; 3(2):313-322. https://doi.org/10.3390/agriengineering3020021
Chicago/Turabian StyleJackman, Patrick, Thomas Lee, Michael French, Jayadeep Sasikumar, Patricia O’Byrne, Damon Berry, Adrian Lacey, and Robert Ross. 2021. "Predicting Key Grassland Characteristics from Hyperspectral Data" AgriEngineering 3, no. 2: 313-322. https://doi.org/10.3390/agriengineering3020021