The agriculture sector has immense potential to improve the
requirement for food and supply healthy and nutritious food. Crop insect
detection is a challenging task for farmers as a significant portion of the
crops is damaged, and the quality is degraded due to the pest attack.
Traditional insect identification has the drawback of requiring well-trained
taxonomists to identify insects based on morphological features accurately.
Experiments were conducted for the classification of nine and 24 insect classes
of the Wang and Xie dataset using the shape features and applying machine
learning techniques such as artificial neural networks (ANN), support vector
machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB) and convolutional
neural network (CNN) model.
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