Use of high-resolution unmanned aerial systems imagery and machine learning to evaluate grain sorghum tolerance to mesotrione
| Autor: | Mithila Jugulam, Isaac Barnhart, Ignacio A. Ciampitti, Sushila Chaudhari, P. V. Vara Prasad, Balaji Aravindhan Pandian |
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| Rok vydání: | 2021 |
| Předmět: |
biology
Contextual image classification business.industry Multispectral image High resolution Sorghum biology.organism_classification Machine learning computer.software_genre Mesotrione Random forest Support vector machine Statistical classification chemistry.chemical_compound chemistry General Earth and Planetary Sciences Artificial intelligence business computer Mathematics |
| Zdroj: | Journal of Applied Remote Sensing. 15 |
| ISSN: | 1931-3195 |
| Popis: | Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aerial systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of our research are to (1) evaluate three supervised classification algorithms [support vector machine (SVM), maximum likelihood, and random forest] for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAS imagery as an alternative to traditional methods of visible herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = − 0.83 to −0.94; p |
| Databáze: | OpenAIRE |
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