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פותח על ידי קלירמאש פתרונות בע"מ -
Detection and counting of flowers on apple trees for better chemical thinning decisions
Year:
2020
Authors :
אלחנתי, ויקטור
;
.
קריקב, עמרי
;
.
Volume :
21
Co-Authors:

Farjon, G., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel; Hillel, A.B., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel;

Facilitators :
From page:
503
To page:
521
(
Total pages:
19
)
Abstract:

Accurate chemical thinning of apple trees requires estimation of their blooming intensity, and determination of the blooming peak date. Performing this task, as of today, requires human experts to be present in the orchards for the entire blossom period or extrapolate using a single observation. Since experts are rare and in high demand, there is a need to automate this process. The system presented in this paper is able to estimate the blooming intensity and the blooming peak date from a sequence of tree images, with close-to-human accuracy. For this purpose, a two years dataset was collected in 2014–2015, partially tagged for the flowers location and completely annotated for blooming intensity. Using this dataset, an algorithm was developed and trained with three stages: a visual flower detector based on a deep convolutional neural network, followed by a blooming level estimator, and a peak blooming day finding algorithm. Despite the challenging conditions, the trained detector was able to detect flowers on trees with an Average Precision (AP) score of 0.68, which is on a par with contemporary results of other objects in detection benchmarks. The blooming estimator was based on a linear regression component, which used the number of flowers detected and related statistics to estimate the blooming intensity. The Pearson correlation between the algorithm blooming estimation and human judgments of several experts indicated high agreement levels (0.78–0.93) which were similar to the correlations measured among the human experts. Moreover, the developed estimator was relatively stable across multiple years. The developed peak date finding algorithm identified correctly the orchard’s blooming peak date, which was used to determine the thinning date in the current practice (the entire orchard is thinned in the same day). Experiments testing the algorithm’s ability to find a blooming peak date for each tree independently showed encouraging results, which may lead upon refinement to a more precise practice for tree-specific thinning. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Note:
Related Files :
Blooming intensity estimation
Chemical thinning
Deep convolutional neural networks
precision agriculture
Visual detection
עוד תגיות
תוכן קשור
More details
DOI :
10.1007/s11119-019-09679-1
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
43676
Last updated date:
02/03/2022 17:27
Creation date:
03/09/2019 13:18
You may also be interested in
Scientific Publication
Detection and counting of flowers on apple trees for better chemical thinning decisions
21

Farjon, G., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel; Hillel, A.B., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel;

Detection and counting of flowers on apple trees for better chemical thinning decisions

Accurate chemical thinning of apple trees requires estimation of their blooming intensity, and determination of the blooming peak date. Performing this task, as of today, requires human experts to be present in the orchards for the entire blossom period or extrapolate using a single observation. Since experts are rare and in high demand, there is a need to automate this process. The system presented in this paper is able to estimate the blooming intensity and the blooming peak date from a sequence of tree images, with close-to-human accuracy. For this purpose, a two years dataset was collected in 2014–2015, partially tagged for the flowers location and completely annotated for blooming intensity. Using this dataset, an algorithm was developed and trained with three stages: a visual flower detector based on a deep convolutional neural network, followed by a blooming level estimator, and a peak blooming day finding algorithm. Despite the challenging conditions, the trained detector was able to detect flowers on trees with an Average Precision (AP) score of 0.68, which is on a par with contemporary results of other objects in detection benchmarks. The blooming estimator was based on a linear regression component, which used the number of flowers detected and related statistics to estimate the blooming intensity. The Pearson correlation between the algorithm blooming estimation and human judgments of several experts indicated high agreement levels (0.78–0.93) which were similar to the correlations measured among the human experts. Moreover, the developed estimator was relatively stable across multiple years. The developed peak date finding algorithm identified correctly the orchard’s blooming peak date, which was used to determine the thinning date in the current practice (the entire orchard is thinned in the same day). Experiments testing the algorithm’s ability to find a blooming peak date for each tree independently showed encouraging results, which may lead upon refinement to a more precise practice for tree-specific thinning. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Scientific Publication
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