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%.
Highlights
Precision agriculture is a management strategy that uses modern science and technology to obtain required agricultural information for efficient precision crop management, such as formula fertilization, precision seeding, pest control, weed removal and water management [1,2,3]
After a pre-classification of the image based on Mahalanobis distance, this section discusses conditional random field (CRF) modeling for image segmentation
To validate the proposed method’s crown segmentation effects on cherry trees, this study compared it with the K-means clustering algorithm, Convolutional Neural Networks (CNN)
Summary
Precision agriculture is a management strategy that uses modern science and technology to obtain required agricultural information for efficient precision crop management, such as formula fertilization, precision seeding, pest control, weed removal and water management [1,2,3]. Target extraction based on RGB (Red Green Blue) digital cameras has seen a wide application in precision farming due to its low cost and non-contact data collecting [16,17,18] In this process, color index-based segmentation techniques are mostly applied to complete the crucial background removal. Qi et al [34] proposed an effective fruit tree segmentation method based on K-means clustering and color features to separate the background from the canopy. This method, is not ideal for the input images that contain weed background.
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