Classification and recognition scheme for vegetable pests based on the BOF-SVM model

Deqin Xiao, Jianzhao Feng, Tanyu Lin, Chunhua Pang, Yaowen Ye

Abstract


The current detection technology for vegetable pests mainly relies on artificial statistics, which exists many shortages such as requiring a large amount of labor, low efficiency, feedback delay and artificial faults. By rapid detection and image processing technology targeting at vegetable pests, not only can reduce manpower and pesticide use, but also provide decision support for precise spraying and improve the quality of vegetables. Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests. Given the above background, this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine (BOF-SVM) on four important southern vegetable pests including Whiteflies, Phyllotreta Striolata, Plutella Xylostella and Thrips. This paper consists of four sub-algorithms. The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation. The second sub-algorithm is to compute the visual vocabulary based on bag of features. The third sub-algorithm is to compute the classifier of pests based on support vector machines. The last one is to classify the pest images using the classifier. In this study, C++ and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm. Experiments showed that the average recognition accuracy was 91.56% for a single image category judgment with 80 images from the real environment, and the average time was 0.39 seconds. This algorithm has achieved the ideal operating speed and precision. It can provide decision support for UAV precise spraying, and also has good application prospect in agriculture.
Keywords: agricultural image processing, vegetable pests, classification, recognition, bag of features, support vector machine
DOI: 10.25165/j.ijabe.20181103.3477

Citation: Xiao D Q, Feng J Z, Lin T Y, Pang C H, Ye Y W. Classification and recognition scheme for vegetable pests based on the BOF-SVM model. Int J Agric & Biol Eng, 2018; 11(3): 190–196.

Keywords


agricultural image processing, vegetable pests, classification, recognition, bag of features, support vector machine

Full Text:

PDF

References


Lv J, Yao Q, Liu Q J, Xue J, Chen H M, Yang B J, et al. Identification of multi-objective rice light-trap pests based on template matching. China J Rice Sci, 2012; 26 (5): 619–623. (in Chinese)

Lou D F, Zhang G M, Jiao Y, Liu X J, Chen Z L, Li Q F, et al. Research on insect image recognition with a common algorithm based on shape and texture. Plant Quarantine, 2012; 26 (4): 10–15. (in Chinese)

Watson A T, O’Neill M A, Kitching I J. Automated identification of live moths (macrolepidoptera) using digital automated identification system (DAISY). Syst. Bio., 2004; 1(3): 287–300. (in Chinese)

Vanhara J, Murarikova N, Malenovsky I, Havel J. Artificial neural networks for fly identification : A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). BIOLOGIA, 2007; 62(4): 462–469.

Cai Q. Research of vegetable leaf-eating pests based on image analysis. MS dissertation, Yangling: Northwest A&F University, 2010; pp.13–34. (in Chinese)

Wang L J. Research on the recognition method of wheat pest image based on LCV and SVM. Shanxi University of Science and Technology, 2013; pp.17–40. (in Chinese)

Wang F. Study of feature extraction and recognition of stored product pests. MS dissertation. Qingdao: Qingdao University of Science & Technology, 2014; pp.11–37. (in Chinese)

Xu X C, Zhao W P, Li C, Wang R P, Wang W. The farmland pests recognition algorithm based on KL transform and BP neural network. Shangxi Science and Technology, 2015; 30(2): 116–119, 132. (in Chinese)

Xie L B. Camellia pest image pattern classification method of the research based on SVM. MS dissertation. Changsha: Central South University of Forestry & Technology, 2015; pp.14–39. (in Chinese)

Xie L B, Yu S J, Zhou G Y, Li H. Classification research of oil-tea pest images based on BoW model. Journal of Central South University of Forestry & Technology, 2015; 35(5): 70–73. (in Chinese)

Chang C, Lin C. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2011; 2(3): 27.

Lin Z R. LIBSVM: SVM classification toolkit. Taiwan University. Taiwan: 2015. http://download.csdn.net/detail/qq_27809141/8702367. Accessed on [2015-05-15].

Lowe D. Object recognition from local scale invariant features. In: Seventh International Conference on Computer Vision (ICCV’99). IEEE Computer Society, 1999; 8(4): 1–8.

Lowe D, Distinctive Image features from scale-invariant keypoints. International Journal of Computer Vision, 2004; 60(2): 91–110.

Tan W X, Zhao C J, Wu H R. Intelligent alerting for fruit-melon lesion image based on momentum deep learning. Multimedia Tools and Applications, 2016; 75(24): 16741–16761.

Deng X L, Lan Y B, Xing X Q, Mei H L, Liu J K, Hong T S. Detection of citrus Huanglongbing based on image feature extraction and two-stage BPNN modeling. Int J Agric & Biol Eng, 2016; 9(6): 20–26.

Esponoza K, Valera D L, Torres J A, Lopez A, Molina-Aiz F D. Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, 2016; 127: 495–505.




Copyright (c) 2018



2023-2026 Copyright IJABE Editing and Publishing Office