Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds

Zhenzhen Cheng, Lijun Qi and Yifan Cheng
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Zhenzhen Cheng: College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China
Lijun Qi: College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China
Yifan Cheng: College of Horticulture, Xinyang Agriculture and Forestry University, No. 1 Beihuan Road, Pingqiao District, Xinyang 464007, China

Agriculture, 2021, vol. 11, issue 5, 1-19

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%.

Keywords: agricultural computer vision; tree-crown segmentation; complex scene; natural orchard environment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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