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Article

Detecting Apples in the Wild: Potential for Harvest Quantity Estimation

1
Department of Geodesy, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
2
Department of Spatial Analysis and Real Estate Market, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
3
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
*
Author to whom correspondence should be addressed.
These authors contributed to the work and should be regarded as the first author.
Academic Editor: Boris Duralija
Sustainability 2021, 13(14), 8054; https://doi.org/10.3390/su13148054
Received: 15 April 2021 / Revised: 14 July 2021 / Accepted: 15 July 2021 / Published: 19 July 2021
(This article belongs to the Special Issue Sustainable Fruit Growing: From Orchard to Table)
Knowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image analysis methods and computational performance, it is possible to create solutions for automatic fruit counting based on registered digital images. The pilot study aims to confirm the state of knowledge in the use of three methods (You Only Look Once—YOLO, Viola–Jones—a method based on the synergy of morphological operations of digital imagesand Hough transformation) of image recognition for apple detecting and counting. The study compared the results of three image analysis methods that can be used for counting apple fruits. They were validated, and their results allowed the recommendation of a method based on the YOLO algorithm for the proposed solution. It was based on the use of mass accessible devices (smartphones equipped with a camera with the required accuracy of image acquisition and accurate Global Navigation Satellite System (GNSS) positioning) for orchard owners to count growing apples. In our pilot study, three methods of counting apples were tested to create an automatic system for estimating apple yields in orchards. The test orchard is located at the University of Warmia and Mazury in Olsztyn. The tests were carried out on four trees located in different parts of the orchard. For the tests used, the dataset contained 1102 apple images and 3800 background images without fruits. View Full-Text
Keywords: computing image analysis; deep learning; yield mapping in an orchard; fruit counting; computer vision computing image analysis; deep learning; yield mapping in an orchard; fruit counting; computer vision
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MDPI and ACS Style

Janowski, A.; Kaźmierczak, R.; Kowalczyk, C.; Szulwic, J. Detecting Apples in the Wild: Potential for Harvest Quantity Estimation. Sustainability 2021, 13, 8054. https://doi.org/10.3390/su13148054

AMA Style

Janowski A, Kaźmierczak R, Kowalczyk C, Szulwic J. Detecting Apples in the Wild: Potential for Harvest Quantity Estimation. Sustainability. 2021; 13(14):8054. https://doi.org/10.3390/su13148054

Chicago/Turabian Style

Janowski, Artur, Rafał Kaźmierczak, Cezary Kowalczyk, and Jakub Szulwic. 2021. "Detecting Apples in the Wild: Potential for Harvest Quantity Estimation" Sustainability 13, no. 14: 8054. https://doi.org/10.3390/su13148054

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