Abstract:
Agriculture is the backbone of every agrarian economy country. Due to the increase in
population, the demand for food is also increasing day by day, and to fulfill that food
requirement, conventional agriculture methods are to be replaced by fast, accurate, and
efficient methods to help the nation's demand and increase exports. From this perspective,
fruit export plays a vital role in agriculture exports and for that fruit counting is a time
consuming and hectic process for cultivators to predict production estimation which not
only requires labor concentration but also extensive money and time. This process can be
catered to by an accurate, fast, and efficient automated system for fruit counting and yield
estimation. In this research study, an algorithm is developed, which detects on-tree fruit
in farms, counts them, and predicts the yield estimation for farmers to streamline their
harvest process. It is implemented in real-time by capturing the images using a webcam
via Raspberry Pi and comparing the results with manual counting and the previous work
done in this field. Algorithm includes K-means clustering for cluster differentiation based
on colors, the color threshold in HSV color space to filter out only the fruit part that is
orange in the present study, and then watershed segmentation to separate clustered fruits.
This algorithm was tested on a dataset of 100 images and then in real-time on sample plant
(oranges) of different lengths, in different light conditions, and compute the average
execution time to process one image. A combined accuracy of 91.23% and an R
2
value of
0.95 was achieved, while the combined average execution time was 1.03 seconds which
showed the algorithm is not only fast but also accurate and efficient.
