Abstract
Researchers proposed various visual based methods for estimating the fruit quantity and performing qualitative analysis, they used ariel and ground vehicles to capture the fruit images in orchards. Fruit yield estimation is a challenging task with environmental noise such as illumination changes, color variation, overlapped fruits, cluttered environment, and branches or leaves shading. In this paper, we proposed a learning free fast visual based method to correctly count the apple fruits tightly overlapped in a complex outdoor orchard environment. We first carefully build the color based HS model to perform the color based segmentation. This step extracts the apple fruits from the complex orchard background and produces the blobs representing apples along with the additional noisy regions. We used the fine tuned morphological operators to refine the blobs received from the previous step and remove the noisy regions fol-lowed by the Gaussian smoothing. Finally we treated the circular shaped blobs with Hough Transform algorithm to calculate the center coordinates of each apple edge and the method correctly locates the apples in the images. The results ensures the proposed algorithm successfully detects and count apple fruits in the images captured from apple orchard and outperforms the standard state of the art contoured based method.
Highlights
The frequently used typical fruit yield estimation methods are usually based on historical data, weather conditions and manually sampling statistics
This paper introduced the concept of circular Hough transform to estimate the curvature to find the apple fruit yield estimation
The proposed method apple fruit yield estimation and counting apples comes to the following conclusions: 1) We proposed a machine learning free pixel classification and curvature analysis algorithm for tightly overlapped apple fruits count
Summary
The frequently used typical fruit yield estimation methods are usually based on historical data, weather conditions and manually sampling statistics. These methods are all time-consuming, requires huge human resource and their prediction results are not accurate enough. The autonomous and accurate visual based fruit yield estimation can help farmers to improve fruit quality through reasonable pruning, designing planting and harvest plan. Visual data such as images are the good source to analyze and monitor the growth of apple fruit in the orchard. We discuss the existing methods that estimates the fruit yield estimation
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More From: International Journal of Advanced Computer Science and Applications
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