A procedure for estimating the number of green mature apples in night-time orchard images using light distribution and its application to yield estimation
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- 作者：Raphael Linker
- 关键词：Artificial vision ; Image analysis ; Fruit detection
- 刊名：Precision Agriculture
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：18
- 期：1
- 页码：59-75
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences;
- 出版者：Springer US
- ISSN：1573-1618
- 卷排序：18
文摘
A procedure for estimating the number of mature apples in orchard images captured at night-time with artificial illumination was developed and its potential for estimating yield was investigated. The procedure was tested using four datasets totaling more than 800 images taken with cameras positioned at three heights. The procedure for detecting apples was based on the observation that the light distribution on apples follows a simple pattern in which the perceived light intensity decreases with the distance from a local maximum due to specular reflection. Accordingly, apple detection was achieved by detecting concentric circles (or parts of circles) in binary images obtained via threshold operations. For each dataset, after calibration of the procedure using 12 images, the estimates of the number of apples were within a few percent of the number of apples counted by visual inspection. Yield estimations were obtained via multi-linear models that used between two and six images per tree. The results obtained using all three cameras were only slightly better than those obtained using only two cameras. Using images from only one side of the tree did not worsen the results significantly. Overall, the yield estimated by the best models was within \(\pm\)10 % of the actual yield. However, the standard deviation of the yield estimation errors corresponded to ~26–37 % of the average tree yield, indicating that improvements are still needed in order to achieve accurate yield estimation at the single-tree level.