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A potential relationship between soil disinfestation efficacy and leaf green reflectance
Steven B Kim1, Steven A Fennimore2, Dong Sub Kim2,3
1Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, United States of America.
Steam soil disinfestation effectively improves plant health, measurable non-destructively using Red, Green, Blue (RGB) image analysis. This method reliably indicates treatment effects on plant health and yield.
Area of Science:
- Agricultural Science
- Plant Pathology
- Horticulture
Background:
- Traditional plant health assessment relies on destructive methods like plant size and yield.
- Soil fumigation poses environmental concerns, driving the need for sustainable alternatives.
- Image analysis offers a non-destructive, efficient method for monitoring plant health.
Purpose of the Study:
- To evaluate steam soil disinfestation as an alternative to chemical fumigation.
- To determine if RGB image analysis can quantify plant health and differentiate treatments.
- To establish RGB image analysis as an early indicator of treatment efficacy.
Main Methods:
- Soil disinfestation treatments included steam, chloropicrin, and a control.
- Plant health was assessed using RGB image analysis focusing on leaf greenness.
- Five distinct RGB color codes from dark to light green were analyzed.
- Results were correlated with total plant yield over two years.
Main Results:
- RGB image analysis of leaf greenness significantly differentiated steam and chloropicrin treatments from the control.
- No significant difference in greenness was observed between steam and chloropicrin treatments.
- The RGB greenness analysis showed a strong correlation with total plant yield.
- Findings were consistent and replicated over two experimental years.
Conclusions:
- RGB image analysis provides a reliable, non-destructive method for assessing plant health post-soil disinfestation.
- Steam disinfestation is a viable alternative to fumigation, with measurable effects on plant health.
- Image analysis can serve as an early, predictive marker for treatment success and crop productivity.
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