Original scientific paper

Automated procedure for early prediction of apple yield in orchardse

Denis Stajnko ; Faculty of Agriculture and Life Sciences, University of Maribor, Maribor, Slovenia *
Tatjana Unuk ; Faculty of Agriculture and Life Sciences, University of Maribor, Maribor, Slovenia
Nina Tojnko ; Faculty of Agriculture and Life Sciences, University of Maribor, Maribor, Slovenia
Simon Kolmanič ; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

* Corresponding author.

Abstract

Procedure for early forecasting of apple yields in Slovenian orchards is presented with the help of a modern application that works in the Android environment and uses a fast 5G transmission network. To recognize fruits in images of ‘Golden delicious’ trees, we used the YOLO pre-learning network, which is based on convolutional neural networks and regression techniques to determine the position of apples in the image. To model the fruit yield, specific cultivar-adjusted growth curve is used, which makes it possible to predict fruit mass from the current and expected fruit diameter and the ratio between diameter and mass. The procedure was tested on a series of 20 images captured from six-years-old orchard revealing on average 71.84% accuracy in fruits counting, 108.27% accuracy in diameter calculating and 97.90% accuracy in yield forecasting. With the first estimation, we have shown that the automated method for yield forecasting is an excellent tool for accurately estimating the yield of an individual plot so we will continue to upgrade it in the future.

Keywords

apple trees; prediction; thickness; model; algorithm

Hrčak ID:

321685

URI

https://hrcak.srce.hr/321685

Publication date:

14.10.2024.

Article data in other languages: croatian

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