Multispectral detection of floral buds for automated thinning of pear

Abstract

Thinning of pome and stone fruit involves the reduction of tree crop load in order to regulate fruit set and quality. As it is typically carried out through manual labor, thinning comprises a large part of a grower's production costs. Mechanized thinning has been shown to be a cost-effective alternative but the performance of existing thinning devices needs to be further improved by taking the variation in bearing capacity of the individual trees into account.In this work, a multispectral camera system is developed to detect the floral buds of pear (cv.Conference) during the growth stages prior to bloom. During a two-year field trial, the multispectral system was used to measure orchard scenes in six distinct optical wavebands under controlled illumination. These wavebands are situated in the visible and near infrared region of the spectrum and were selected based on hyperspectral laboratory measurements described in previous work.The recorded multispectral images were converted to a database containing the spatial-spectral signatures of the objects present in the orchard. Subsequently, canonical correlation analysis was applied to create a spectral discriminant model that detects pixels originating from floral buds. This model was then applied to the recorded data after which an image analysis algorithm was designed and optimized to predict the number of floral buds. In total, approximately 87% of the visible floral buds were detected correctly with a low false discovery rate (<16 %). Therefore, it is expected that the multispectral sensor can be used to improve the efficiency of existing thinning devices.Additionally, it could as well be used as a stand-alone sensor for early-season yield estimation.

Characterization and Quantification of Polyphenols and Triterpenoids in Thinned Young Fruits of Ten Pear Varieties by UPLC-Q TRAP-MS/MS

“…For this reason, thinning of pear trees is a cultural practice often adopted in all orchards to remove excess and undesirable fruit. Thinning is one of the most effective measures to improve fruit size and quality at harvest and to balance the pear yield in the following year [3]. According to the data published by the United Nations Food and Agriculture Organization, in 2016, the total harvesting area and pear production worldwide was 1584.96 thousand hectares and 27.35 million tonnes, respectively.…”

Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection

“…Fruit detection is important in many agricultural tasks such as yield monitoring [1,2,3,4,5,6,7,8], phenotyping [9,10,11], precision agriculture operations (e.g., spraying [12] and thinning [13,14,15]), and robotic harvesting [16,17,18]. Despite intensive research conducted in identifying fruits, implementing a real-time vision system remains a complex task [17,18].…”

Automation and robotics in the cultivation of pome fruit: Where do we stand today?

“…In this paper, only mechanical thinning will be discussed, because of its lower environmental impact than the chemical alternative. For other methods the reader may consult (Byers et al, 1986; Greene et al, 2013; Wouters, 2014).…”

“…Moreover, it has the additional downside of shaking the largest fruitlets away, due to a higher inertia. However, these large fruitlets have the highest potential of reaching high quality and are preferably not removed (Wouters, 2014).…”

“…Thinning: (a) example of the Darwin machine (Mechanical blossom thinner, 2020); (b) example of the Baum machine (Damerow et al, 2007); (c) prototype of thinning machine with pressured air (Wouters, 2014); (d) the concept of the end effector for the thinning machine of Yang (2012) [Color figure can be viewed at wileyonlinelibrary.com]…”