This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic stud...This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.展开更多
Target detection is one of research focuses for precision chemical application.This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging.In processing the image data with ENVI s...Target detection is one of research focuses for precision chemical application.This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging.In processing the image data with ENVI software,after dimension reduction,noise reduction,de-correlation for high-dimensional data,and selection of the region of interest,the SAM(Spectral Angle Mapping)model was built for automatic identification of cabbages and weeds.With the HSI(Hyper Spectral Imaging)Analyzer,the training pixels were used to calculate the average spectrum as the standard spectrum.The parameters of the SAM model,which had the best classification results with 3-point smoothing,zero-order derivative,and 6-degrees spectral angle,was determined to achieve the accurate identification of the background,weeds,and cabbages.In comparison,the SAM model can completely separate the plants from the soil background but not perfect for weeds to be separated from the cabbages.In conclusion,the SAM classification model with the HSI analyzer could completely distinguish weeds from background and cabbages.展开更多
Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management ...Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.展开更多
文摘This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.
基金This research was financially supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA101904)the National High Technology Research and Development Program of China(984 Program)(2011-G32).
文摘Target detection is one of research focuses for precision chemical application.This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging.In processing the image data with ENVI software,after dimension reduction,noise reduction,de-correlation for high-dimensional data,and selection of the region of interest,the SAM(Spectral Angle Mapping)model was built for automatic identification of cabbages and weeds.With the HSI(Hyper Spectral Imaging)Analyzer,the training pixels were used to calculate the average spectrum as the standard spectrum.The parameters of the SAM model,which had the best classification results with 3-point smoothing,zero-order derivative,and 6-degrees spectral angle,was determined to achieve the accurate identification of the background,weeds,and cabbages.In comparison,the SAM model can completely separate the plants from the soil background but not perfect for weeds to be separated from the cabbages.In conclusion,the SAM classification model with the HSI analyzer could completely distinguish weeds from background and cabbages.
文摘Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.
