A new soil-plant image segmentation approach using K-means applied to experimental plots

Abstract

The image segmentation procedure is fundamental in the phenotyping of plant images. Supervised algorithms have been used for pixel soil plant segmentation. Recent research has used the K-means algorithm to evaluate the segmentation of agronomic images in different crops with different databases. The algorithm has shown good performance in the pixel clustering process despite not being able to classify them directly. The present research intends to propose the use of the K-means algorithm in image segmentation and pixel classification in sugarcane images. 37,430-pixel samples referring to soil and vegetation were manually extracted from some images. This information was used to train and evaluate supervised models. The model with the best performance was considered the "standard" model. A rule that can serve as empirical support to interpret the clusters formed by K-means by assigning a label to each pixel was proposed. Then K-means was used to segment all images and classify the pixels. The vegetation index was used as features and the standard model classification was used as a true label. The measures recall, F1Score, precision, and accuracy were used as a performance measure of K-means, and the mask of each produced to compare the final result of the two approaches, highlighting the vegetation. Using K-means provided better-defined edges than Logistic Regression (standard model) and considerably distinguished the occurrence of soil between the leaves, with precision ranging from 0.77 to 0.92. These results expressed the importance of vegetation index to the clusterization process and showed that K-means ally to an interpretation clusters rule, which could be used to classify pixels in images.