Abstract
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.
Highlights
In citrus groves, yield estimation is typically carried out a few weeks earlier to fruitage to estimate the resource requirement
As orange juice needs to be processed within 48 hours of harvesting, orange juice manufacturers need suppliers to provide accurate yield estimates to guarantee that their juice plants can run at full capacity given the time constraints
Image processing can help in improving the decision making process for irrigation, fruit sorting and yield estimation [1]
Summary
Yield estimation is typically carried out a few weeks earlier to fruitage to estimate the resource requirement. As orange juice needs to be processed within 48 hours of harvesting, orange juice manufacturers need suppliers to provide accurate yield estimates to guarantee that their juice plants can run at full capacity given the time constraints. That poses a challenge when detection is based on filtering of orange color only Lighting may pose another challenge as the oranges may appear differently, under varying lighting conditions, such as bright sunlight, cloudy, and evening. The images are more consistent in terms of brightness and intensity changes. While in the latter case, things may look different if they are exposed to direct sunlight as opposed to when they are under a shadow. A need arises for a way to count two or more smaller citrus blobs as one, while in the later case we need to break down a larger blob into smaller units, where each unit is counted as a separate fruit
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