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Article

Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation

1
Metacortex S.r.l., Via dei Campi 27, 38050 Torcegno, Italy
2
Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9, 38123 Trento, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4214; https://doi.org/10.3390/s20154214
Received: 10 July 2020 / Revised: 24 July 2020 / Accepted: 28 July 2020 / Published: 29 July 2020
(This article belongs to the Section Sensing and Imaging)
Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming. View Full-Text
Keywords: kiwifruit; fruit counting; yield estimation; computer vision fruit detection kiwifruit; fruit counting; yield estimation; computer vision fruit detection
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MDPI and ACS Style

Mekhalfi, M.L.; Nicolò, C.; Ianniello, I.; Calamita, F.; Goller, R.; Barazzuol, M.; Melgani, F. Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation. Sensors 2020, 20, 4214. https://doi.org/10.3390/s20154214

AMA Style

Mekhalfi ML, Nicolò C, Ianniello I, Calamita F, Goller R, Barazzuol M, Melgani F. Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation. Sensors. 2020; 20(15):4214. https://doi.org/10.3390/s20154214

Chicago/Turabian Style

Mekhalfi, Mohamed Lamine, Carlo Nicolò, Ivan Ianniello, Federico Calamita, Rino Goller, Maurizio Barazzuol, and Farid Melgani. 2020. "Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation" Sensors 20, no. 15: 4214. https://doi.org/10.3390/s20154214

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