The effects of mechanical defoliation and pinching (1 cm tip cutting) on Buxus plant growth, nutrient mobilization, and root architecture were determined. When 100% defoliation was applied, the highest increase rates of 80.3% in shoots and 88% in leaves were observed compared to the control group. In contrast, the overall effects of defoliation and pinching were negative, with 100% defoliation having the most negative effects. The chlorophyll content of the newly formed young leaves was also 50% lower with 100% defoliation. Leaves and root nutrient mobilization changed significantly, depending on the effects of defoliation and pinching. Apart from a very small increase in root length and number of forks, the effects of the treatments were negative, with 100% defoliation having the greatest negative effect on root development. Most affected was the number of crossings, which was 78% lower than in the control. In addition, machine learning (ML) algorithms were used in the study, including multilayer perceptron, J48, PART, and logistic regression. The input variables were evaluated to model and predict the root features. The performance values of the ML algorithms were noted in the following order: Logistic Regression> PART> J48> MultilayerPerceptron. As the severity of defoliation increased, the losses of the plant also increased. However, boxwood has mechanisms to compensate for these losses even when it suffers complete defoliation.
Effects of mechanical defoliation and pinching applications on plant growth and root system analysis with machine learning in boxwoods
Published 2024 in BioResources
ABSTRACT
PUBLICATION RECORD
- Publication year
2024
- Venue
BioResources
- Publication date
2024-08-22
- Fields of study
Not labeled
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- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- boxwood
The plant species studied for responses to defoliation and pinching treatments.
Aliases: Buxus
- chlorophyll content
The amount of chlorophyll measured in leaves, used as an indicator of photosynthetic capacity.
- j48
A decision tree machine learning algorithm used for predicting root features.
Aliases: J48 decision tree
- leaf growth
The increase in leaf number or size measured as a plant growth outcome.
- logistic regression
A statistical machine learning algorithm used for predicting root features, found to have the best performance.
- machine learning algorithms
Computational algorithms including logistic regression, J48, PART, and multilayer perceptron used to model and predict root features.
Aliases: ML algorithms
- mechanical defoliation
The experimental treatment of mechanically removing leaves from plants at varying severity levels.
Aliases: defoliation
- multilayer perceptron
A neural network-based machine learning algorithm used for predicting root features.
Aliases: MultilayerPerceptron, MLP
- nutrient mobilization
The movement and redistribution of nutrients within leaves and roots in response to treatments.
- part
A rule-based machine learning algorithm used for predicting root features.
Aliases: PART rule learner
- pinching
The experimental treatment of cutting 1 cm from plant tips to study growth effects.
Aliases: tip cutting
- root architecture
The structural characteristics of the root system including length, forks, and crossings.
- shoot growth
The increase in shoot number or size measured as a plant growth outcome.
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