Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds

Abstract

Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vital for precision pesticide application. However, the extant studies do not provide an efficient and reliable method of extracting individual trees with irregular tree-crown shapes and complicated backgrounds. This paper on our study proposes a Mahalanobis distance and conditional random field (CRF)-based segmentation model to extract cherry trees accurately in a natural orchard environment. This study computed Mahalanobis distance from the image’s color, brightness and location features to acquire an initial classification of the canopy and background. A CRF was then created by using the Mahalanobis distance calculations as unary potential energy and the Gaussian kernel function based on the image color and pixels distance as binary potential energy. Finally, the study completed image segmentation using mean-field approximation. The results show that the proposed method displays a higher accuracy rate than the traditional algorithms K-means and GrabCut algorithms and lower labeling and training costs than the deep learning algorithm DeepLabv3+, with 92.1%, 94.5% and 93.3% of the average P, R and F1-score, respectively. Moreover, experiments on datasets with different overlap conditions and image acquisition times, as well as in different years and seasons, show that this method performs well under complex background conditions, with an average F1-score higher than 87.7%.

Image Segmentation of UAV Fruit Tree Canopy in a Natural Illumination Environment

“…To validate the proposed method's crown segmentation performance on fruit trees, this study compared it with the K-means clustering algorithm and GMM (Gaussian mixture model) algorithm, which were unsupervised image segmentation methods that are widely used for fruit crown segmentation [42,44,63]. AOCC, POCC, FOCC, MOCC, and ROCC are combined into a feature matrix M as the segmentation feature of K-means and GMM algorithms.…”

“…The authors compared the proposed method with the results of FCN segmentation and the results showed that that the model can improve the detection rate of canopy by 34.7%. In a word, supervised learning can achieve highprecision segmentation, but it relies on complex feature engineering or large-size labeled data [42]. Yuzhen et al [43] summarized 34 data sets of machine vision in agriculture, among which there are no open datasets for fruit tree segmentation as yet.…”

Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China

“…Various studies have focused on image segmentation based on scenes. The current agricultural field segmentation and delineation approaches can be mainly divided into three categories: threshold-based methods [7][8][9], texture analysis technique methods [10][11][12][13], and trainable classification model methods [14][15][16][17]. The development of deep neural networks provided new opportunities for agricultural scene extraction by extracting the target features automatically to save the pre-processing costs, and their architectures are highly adaptive to complex problems [18].…”

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism

“…The manual-feature-based image segmentation method can only use limited features such as color information, texture information, and spatial structure of images for image segmentation due to the limited computational performance of the computer. This process is time-consuming and ineffective in more complex cropland segmentation, such as threshold segmentation [ 7 , 8 , 9 ], texture analysis [ 10 , 11 , 12 , 13 ], edge extraction [ 14 , 15 , 16 ], and region-based segmentation [ 17 ].…”