The harvest process for most fresh-market tree fruit crops is labor-intensive and time-consuming. For sweet cherry (Prunus avium L.), harvest costs generally account for 50% to 60% of total production costs (Seavert et al., 2008), yet despite its singular importance in fruit production budgets, harvest efficiency is not well studied. Prototype mechanical and robotic systems have been tested for fruit trees to improve harvest efficiency (Brown, 2005; Burks et al., 2005; Li et al., 2011; Sanders, 2005). For sweet cherry, a prototype harvester (Peterson and Wolford, 2001) and a greenhouse robotic harvesting system (Tanigaki et al., 2008), which require minimal or no human intervention in their operation, were manufactured for trial purposes. There are issues with mechanical harvesting systems such as fruit and tree damage, difficulties in selective harvest, cost of machinery, and the need for specific tree architecture. Preliminary testing with a prototype mechanical harvester for sweet cherry revealed the potential to reduce harvest costs by 90% (Seavert and Whiting, 2011). Nevertheless, all sweet cherries for fresh-market consumption are currently harvested manually (Seavert et al., 2008).
Manually harvesting sweet cherries is one of the most labor-intensive of all agricultural endeavors as a result of large tree size, high number of fruit per tree, and small fruit size. During harvest, laborers move along rows picking fruit with a slight twisting–snapping motion, removing fruit by the pedicel in clusters of one to four. Fruit are placed into metal or plastic buckets (≈9 kg capacity) that are secured over their shoulders with strapping. When necessary to access fruit, aluminum ladders, ranging in height from 2.4 to 4 m, are carried and used by pickers. When the picking bucket is full, pickers dump fruit into a larger receptacle (either a plastic lug or bin designed to hold ≈11.60 kg or 180 kg, respectively). When used, full lugs are dumped subsequently into bins, and bins are collected by tractor, loaded on a trailer, and delivered to a local packing shed for sorting, cleaning, and packaging.
Intuitively, there are many factors that will affect harvest efficiency. These may be biological, technological, and sociological, yet there are limited empirical reports of their impact in tree fruit. Among others, tree architecture and age, fruit load, and picker skill are important factors affecting harvest efficiency (Ampatzidis et al., 2012a; Strik and Buller, 2002). Strik et al. (2003) found that pruning method affected hand harvest efficiency of blueberries (‘Bluecrop’ and ‘Berkeley’), in some cases by more than 50%. New high-density training systems for sweet cherry have been introduced in an attempt to produce high-quality fruit and achieve earlier orchard productivity (Lang, 2005; Whiting et al., 2005; Whiting and Smith, 2007). Whiting (2009) described the UFO system, a planar architecture comprised of unbranched vertical wood designed to improve worker safety and harvest efficiency and facilitate the incorporation of mechanization and automation technologies. However, there are limited published reports on the role of canopy architecture on harvest labor efficiency and safety (Ampatzidis et al., 2012a). This is attributable in part to the complexity of collecting reliable data in the field during hand harvest. Data acquisition systems should be automated and timely, eliminate manual entry of data, increase data accuracy, and prevent employee fraud (Ampatzidis, 2010; Ampatzidis et al., 2008).
Various approaches to, and systems for, data collection in orchards such as yield monitoring systems for citrus (Salehi et al., 2000; Schueller et al., 1999; Whitney et al., 1999), in-field traceability systems based on radio frequency identification (RFID) and barcodes registration technologies (Ampatzidis and Vougioukas, 2009; Ampatzidis et al., 2009), tree identification systems (Bowman, 2010; Luvisi et al., 2010, 2011), wearable systems for tracing and locating workers (Ampatzidis et al., 2011) as well as wireless and mobile acquisition devices (Cunha et al., 2010; Kuflik et al., 2009; Morais et al., 2008) have been developed and field-tested. A prototype system for measuring average harvest efficiency, per picking crew, was developed in 2010 (Ampatzidis et al., 2012a) and modified in 2011 (Ampatzidis et al., 2012b) to a real-time LMS with the ability to track and record individual picker efficiency. In the current study, the LMS was used to investigate the role of tree architecture on picker efficiency during sweet cherry harvest in commercial orchards.
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