The DT Technique

Decision Trees are a non-parametric supervised learning method used for classification and regression. They partition the data into subsets based on feature value tests.

• Functionality: In plant disease detection, DTs classify plant health by learning decision rules inferred from the data features. Each node in the tree represents a feature, each branch a decision rule, and each leaf a class label (healthy or diseased).

• Advantages: DTs are easy to understand and interpret. They require little data preprocessing and are capable of handling both numerical and categorical data.

Techniques based on computer vision for identifying and classifying plant diseases [2]