Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method

Abstract

Visual navigation is developing rapidly and is of great significance to improve agricultural automation. The most important issue involved in visual navigation is extracting a guidance path from agricultural field images. Traditional image segmentation methods may fail to work in paddy field, for the colors of weed, duckweed, and eutrophic water surface are very similar to those of real rice seedings. To deal with these problems, a crop row segmentation and detection algorithm, designed for complex paddy fields, is proposed. Firstly, the original image is transformed to the grayscale image and then the treble-classification Otsu method classifies the pixels in the grayscale image into three clusters according to their gray values. Secondly, the binary image is divided into several horizontal strips, and feature points representing green plants are extracted. Lastly, the proposed double-dimensional adaptive clustering method, which can deal with gaps inside a single crop row and misleading points between real crop rows, is applied to obtain the clusters of real crop rows and the corresponding fitting line. Quantitative validation tests of efficiency and accuracy have proven that the combination of these two methods constitutes a new robust integrated solution, with attitude error and distance error within 0.02° and 10 pixels, respectively. The proposed method achieved better quantitative results than the detection method based on typical Otsu under various conditions.

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation

“…Although DNNs have good performance in accuracy, they really require large computing resources, and this limits their practical applications (Zhang et al, 2018a;Roy et al, 2019;Pan et al, 2021). Yu et al (2021) proposed a treble classification and twodimensional clustering-based crop rows detection in paddy fields for the problem of numerous weeds and floating weeds in the paddy fields. This method used a triple Otsu's (Otsu, 2007) method approach for segmentation and fitted the detection lines after selecting the misleading points by a two-dimensional adaptive clustering method.…”

“…Yu et al ( 2021 ) proposed a treble classification and two-dimensional clustering-based crop rows detection in paddy fields for the problem of numerous weeds and floating weeds in the paddy fields. This method used a triple Otsu's (Otsu, 2007 ) method approach for segmentation and fitted the detection lines after selecting the misleading points by a two-dimensional adaptive clustering method.…”

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

“…The scalability and efficiency of the K-means algorithm make it suitable for processing large datasets in cropland [97]. However, it has been noted that the K-means algorithm assumes that the clusters are spherical, equally sized, and have similar densities, which can lead to over-clustering or under-clustering in certain situations [98]. In recent years, several studies have attempted to address the limitations of traditional clustering algorithms in crop row detection.…”

Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

“…The OS method is a straightforward, stable, and effective threshold-based segmentation algorithm widely used for image processing applications [46]. Initially, a greyscale his-togram is used to divide the image into two classes-background and target objects [47,48]. Next, an exhaustive search selects an optimal threshold that maximizes separability and minimizes variance within classes.…”