The SVM Technique

SVM is a supervised learning model used for classification and regression. It constructs a hyperplane or set of hyperplanes in a high-dimensional space to separate different classes.

• Functionality: In plant disease detection, SVM is trained to classify plant health by analyzing image features. The algorithm finds the optimal hyperplane that best separates the healthy plants from the diseased ones.

• Advantages: SVM is effective in high-dimensional spaces and is still effective when the number of dimensions is greater than the number of samples. It is also memory efficient as it uses a subset of training points in the decision function (support vectors).