Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
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
According to the requirements of agricultural production and usem, taking diagnosis and decision-making of prevention for common diseases and pests in fruits and vegetables in southern China as the core, with communic...According to the requirements of agricultural production and usem, taking diagnosis and decision-making of prevention for common diseases and pests in fruits and vegetables in southern China as the core, with communication and sharing as principle, adopted diagnosis, inquiries and guiding prevention of diseases and pests in fruits and vegetables as purpose, expert examination system of plant disease and pests in fruits and vegetables based on Web highly integrates the knowledge and prevention techniques of common diseases and pests for main fruit and vegetable in south China. In this system, the users can browse and inquiry the information about the fruit and vegetable diseases and pests, as well as their diagnosis and control. The implementation of the system plays an active role in promo- ting plant protection knowledge and guiding farms to scientifically control diseases and pests in fruits and vegetables展开更多
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
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.
基金Supported by Science and Technology Project of Guangdong Province(2007A020300002-12)~~
文摘According to the requirements of agricultural production and usem, taking diagnosis and decision-making of prevention for common diseases and pests in fruits and vegetables in southern China as the core, with communication and sharing as principle, adopted diagnosis, inquiries and guiding prevention of diseases and pests in fruits and vegetables as purpose, expert examination system of plant disease and pests in fruits and vegetables based on Web highly integrates the knowledge and prevention techniques of common diseases and pests for main fruit and vegetable in south China. In this system, the users can browse and inquiry the information about the fruit and vegetable diseases and pests, as well as their diagnosis and control. The implementation of the system plays an active role in promo- ting plant protection knowledge and guiding farms to scientifically control diseases and pests in fruits and vegetables