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Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability
- Source :
- Agronomy, Vol 11, Iss 583, p 583 (2021), Agronomy, Volume 11, Issue 3
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Glyphosate is the most widely used herbicide in crop production due to the widespread adoption of glyphosate-resistant (GR) crops. However, the spray of glyphosate onto non-target crops from ground or aerial applications can cause severe injury to non-GR corn plants. To evaluate the crop damage of the non-GR corn plants from glyphosate and the recoverability of the damaged plants, we used the hyperspectral imaging (HSI) technique in field experiments with different glyphosate application rates. This study investigated the spectral characteristic of corn plants and assessed the corn plant damage from glyphosate. Based on HSI image analysis, a spectral variation pattern was observed at 1 week after treatment (WAT), 2 WAT, and 3 WAT from the glyphosate-treated non-GR corn plants. It was further found that the corn plants treated with glyphosate rates equal to or higher than 0.5X (X = 0.866 kilograms acid equivalents/hectare (kg ae/ha) represents the recommended spray rate for GR corn) would suffer unrecoverable damage. Using the Jeffries–Matusita distance as the spectral sensitivity criterion, three sensitive bands from the measured spectra were selected to create two spectral indices for crop recoverability differentiation in band ratio and normalization forms, respectively. With the two spectral indices, the corn plants recoverable and unrecoverable from damage were classified with an overall accuracy greater than 95%. Then, three machine learning algorithms (k-nearest neighbors, random forest, and support vector machine) were respectively combined with the successive projections algorithm to create models to relate selected feature spectral bands to glyphosate spray rates. The results indicated that the models achieved reasonable accuracy, especially in the group of recoverable plants. This study illustrated the potential of the hyperspectral imaging technique for evaluating crop damage from herbicides and recoverability of the injured plants using different data analysis and machine learning modeling approaches for practical weed management in crop fields.
- Subjects :
- 0106 biological sciences
Machine learning
computer.software_genre
01 natural sciences
lcsh:Agriculture
Crop
chemistry.chemical_compound
glyphosate
Hectare
Mathematics
Severe injury
business.industry
lcsh:S
Hyperspectral imaging
04 agricultural and veterinary sciences
Spectral bands
Weed control
corn
hyperspectral
machine learning
chemistry
Glyphosate
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
recoverability
Artificial intelligence
business
Agronomy and Crop Science
computer
After treatment
010606 plant biology & botany
Subjects
Details
- ISSN :
- 20734395
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Agronomy
- Accession number :
- edsair.doi.dedup.....e7731f5c5b708ad8d0267b768b5933dd