Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting

Abstract

Fresh market sweet cherry harvesting is a labour-intensive operation that accounts for more than 50% of annual production costs. To minimise labour requirements for sweet cherry harvesting, mechanized harvesting technologies are being developed. These technologies utilise manually-placed limb actuators that apply vibrational energy to affect fruit release. Machine vision-based automated harvesting system have potential to further reduce harvest labour through improving efficiency by eliminating manual handling, positioning and operation of the harvester and/or harvesting mechanism. A machine-vision system was developed to segment and detect cherry tree branches with full foliage, when only intermittent segments of branches were visible. Firstly, an image segmentation method was developed to identify visible segments of the branches. Bayesian classifier was used to classify image pixels into four classes -branch, cherry, leaf and background. The algorithm achieved 89.6% accuracy in identifying branch pixels. The length and orientation of branch segments were then analysed to link individual sections of the same branch together and to represent the branches with an

Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision

“…The setup used for imaging in the Y-trellis system is shown in Figure 2. Detailed description about imaging setup can be found in [15,16]. mounted in an electric vehicle.…”

“…Detection of cherry tree branches was carried out in color images acquired in the test orchards using methods developed by [15,16] (Figure 3). The cherry tree branches were covered by dense foliage, which limited the visibility of branches in tree canopies.…”

“…Using these features as clues, the branch sections belonging to a branch were identified, and a model equation was fitted to reconstruct the branch. Detailed explanation of this method can be found in [15]. This method detected most of the branches when a few sections were exposed to the camera.…”

“…The development of a robust image processing system that can detect branches and a method to automatically locate shaking positions in the tree branches is the desirable next step towards fully automated tree fruit harvesting with a mechanical shaker. [15] developed a method of detecting cherry tree branches often covered with leaves and fruit using morphological properties of visible branch segments and reported a detection accuracy of 89% in vertical planar architecture. [16] further improved the branch detection method by using location of cherry clusters as a clue to detect heavily or completely occluded branches in high foliage density orchards trained in Upright Fruiting Offshoots (UFO) architecture.…”

Extracción de Características en Viñedos Mediante Algoritmos de Clasificación Difusa y Técnicas de Inteligencia Artificial

“…Por ejemplo, Reis et al (2012) detecta uvas, en imágenes tomadas durante la noche, usando un algoritmo que establece rangos de color para la uva, rangos que no son aplicables a otros viñedos ya que son producto de prueba y error para un viñedo en particular y en un estado de desarrollo particular. Para otro frutal, cerezos en particular y tendiente a detectar ramas individuales, se ha recurrido a la misma estrategia de capturar imágenes durante la noche y con iluminación artificial (Amatya et al, 2016) para evitar los problemas ocasionados por la variabilidad de las condiciones de iluminación dentro de la escena.…”