In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and...In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds.This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color images.Agronomic images of two different databases were used for the segmentation algorithms.Using the thresholding technique,everything except plants was removed from the images.Afterward,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm.The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms.The proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,respectively.The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.展开更多
Compared with highly subjective manual sensory quality evaluation,the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea qualit...Compared with highly subjective manual sensory quality evaluation,the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea quality evaluation system.In this study,Yinghong No.9 black tea was taken as the research object,and the gold pekoe,color and strips were adopted as the appearance evaluation characteristics for black tea.An image segmentation method based on the improved K-means clustering algorithm was proposed to realize the segmentation of the dark background area,tea area and golden pekoe area.The CIELAB color model was used to extract color features of the tea area.The texture features extracted by GLRLM were applied to evaluate the strips.The RF,SVR and BPNN were selected to construct prediction models for evaluating tea appearance quality.The prediction accuracy and generalization ability of the RF model are superior to those of the SVR model and BP model,with R2p,RMSEP and RPD values of 0.898,1.548 and 3.207,respectively.The proposed feature extraction method based on regional segmentation intuitively described the key evaluation characteristics of black tea appearance,and the predicted results were highly consistent with the manual sensory evaluation.展开更多
文摘In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds.This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color images.Agronomic images of two different databases were used for the segmentation algorithms.Using the thresholding technique,everything except plants was removed from the images.Afterward,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm.The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms.The proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,respectively.The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.
基金supported by Zijin County Science and Technology Plan Project,Guangdong Modern Agricultural Industrial Technology System Innovation Team Building Project(Tea)with agricultural products as the unit(2024KJ120)Qingyuan City Science and Technology Plan Project(2022KJJH065)Heyuan City Science and Technology Plan Project(Heke 2021030).
文摘Compared with highly subjective manual sensory quality evaluation,the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea quality evaluation system.In this study,Yinghong No.9 black tea was taken as the research object,and the gold pekoe,color and strips were adopted as the appearance evaluation characteristics for black tea.An image segmentation method based on the improved K-means clustering algorithm was proposed to realize the segmentation of the dark background area,tea area and golden pekoe area.The CIELAB color model was used to extract color features of the tea area.The texture features extracted by GLRLM were applied to evaluate the strips.The RF,SVR and BPNN were selected to construct prediction models for evaluating tea appearance quality.The prediction accuracy and generalization ability of the RF model are superior to those of the SVR model and BP model,with R2p,RMSEP and RPD values of 0.898,1.548 and 3.207,respectively.The proposed feature extraction method based on regional segmentation intuitively described the key evaluation characteristics of black tea appearance,and the predicted results were highly consistent with the manual sensory evaluation.
