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 ...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.展开更多
基金This work was supported by the National Spark Program(2015GA780002)Guangdong Province Science and Technology Program(2015A020224042).
文摘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.
