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A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

Information Processing in Agriculture 2022年第3期9卷 355-364页

作者： Sovi Guillaume Sodjinou Vahid Mohammadi Amadou Tidjani Sanda Mahama Pierre Gouton Institute of Mathematics and Physical Sciences University of Aboney CalaviDangboBenin ImViA UFR Sciences et TechniquesUniversite de BourgogneFranche-ComteDijonFrance

In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of *** 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 *** 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 of two different databases were used for the segmentation *** the thresholding technique,everything except plants was removed from the ***,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive *** comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels *** 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,*** 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.

关键词： weed coverage Semantic segmentation Convolutional neural network Subtractive clustering algorithm Simple Linear Iterative Clustering (SLIC) K-means

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