{'fraction_non_alphanumeric': 0.017167381974248927, 'fraction_numerical': 0.0, 'mean_word_length': 6.216494845360825, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Field pest identification by an improved Gabor texture segmentation scheme

Abstract Segmentation of pests is a critical step in using machine vision for field automation tasks. A new method called GaborBoostSVM is proposed in this paper. The method comprises Gabor wavelets‐based feature extraction, AdaBoost‐based feature selection and SVM‐based pattern recognition algorithm. It performed unsupervised classification in field images into pest and background categories for real‐time selective insecticide application. The results showed that the method is capable of performing texture‐based pest and background classification consistently high, effectively and with high classification accuracy.