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The automatic detection and classification of insect pest is emerged as one of the interesting research areas in agriculture sector to ensure reduction of damages due to pest. From the general process of detection of pest, feature extraction plays a significant role. It extracts features from the segmented image obtained by segmentation process, and then extracted images are being transferred to a classifier for the operations. In this work, we studied and implemented two feature extraction techniques, i.e., Histogram of Oriented Gradient (HOG) and Local Binary Pattern techniques (LBP). The comparison result expressed that HOG performs better than its counterpart. The result comes with accuracy of 97% for HOG. Here, we are adopting SVM-based pest classification as a test case.

Insect pests are posing a significant threat to agricultural production. They live in different places like fruits, vegetables, flowers, and grains. It impacts plant growth and causes damage to crop yields. We presented an automatic detection and classification of tomato pests using image processing with machine learning-based approaches. In our work, we considered texture features of pest images extracted by feature extraction algorithms like gray level co-occurrence matrix (GLCM), local binary pattern (LBP), histogram of oriented gradient (HOG), and speeded up robust features (SURF). The three standard classification methods, including support vector machine (SVM), k-nearest neighbour (k-NN), and decision tree (DT) are used for classification operation. The three classifiers have undergone a comprehensive analysis to present which classifier with which feature yields the best accuracy. The experiment results showed that the SVM classifier's precision using the feature extracted by local binary patterns (LBP) algorithm achieves the highest value of 81.02%. MATLAB software used for feature extraction and waikato environment for knowledge analysis (WEKA) graphical user interface for classification.

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