Development and Preliminary Evaluation of a New Bin Filler for Apple Harvesting and In-Field Sorting Machine

International Journal of Agricultural and Biological Engineering

Wang

et al. 2019

Self Cite

An increasing number of U.S. apple growers are now interested in using harvest platforms to increase harvest productivity, expand labor pool, and alleviate tough working conditions. To maximize benefits, functions of thinning, pruning & training, and infield sorting have been or are to be incorporated into harvest platforms. Though growers are most concerned with economic benefits, few cost-benefit studies had been conducted on different platforms. In the meantime, economic analysis procedure is complex and each analysis is for one specific machine (not for general purposes). No software has been developed as a general and ready-to-use tool for growers and researchers for the platform economic analysis. In this study, platforms, both available on the marketplace and developed in lab as pilot trials, were reviewed. Costs and benefits models were then established, based on which multipurpose apple harvest platform economic evaluation software (iMPAHP) was developed (capable of evaluating a wide variety of apple harvest platforms). A case study (machine cost of $100 000, accommodating 6 workers, processing apple incidence of 10% with 90% sort-out rate, and harvest, thinning, and pruning & training productivity increase by 40%, 50%, and 60%, respectively) based on iMPAHP demonstrated that infield sorting, harvest, thinning, and pruning and training accounted for 48.4%, 23.9%, 14.3%, and 13.4% of the total benefits, respectively. In the case that the platform was in all-four-purpose-application, the net present value (NPV) analysis of a 10-year investment showed a positive return of $60 547. However, without infield sorting function, the NPV resulted in a negative value, indicating a loss for the machine investment. Though incorporating the modular infield sorting system certainly increased the overall machine investment by $30 000, the benefits outweighed the costs.

Section: Review Of Multi-purpose Apple Harvest Platformsmentioning

confidence: 99%

Models for economic evaluation of multi-purpose apple harvest platform and software development

International Journal of Agricultural and Biological Engineering

Wang

et al. 2019

Self Cite

Applied Engineering in Agriculture

“…A new bin filler was recently developed for an apple harvest and infield sorting machine ( fig. 11) (Zhang et al, 2017b(Zhang et al, , 2017c(Zhang et al, , 2017d. After being singulated and passing the machine vision inspection chamber, apples fall freely into a pair of spinning foam rollers, which decelerate the fruit and then distribute them in the bin uniformly through a spinning pinwheel mounted with floating pads.…”

Section: Prototypementioning

confidence: 99%

A Review of Bin Filling Technologies for Apple Harvest and Postharvest Handling

Zhang¹,

Pothula²,

Lu³

2018

Self Cite

Bin fillers play a critical role in transporting and distributing fruit evenly and without bruising into individual bins or containers from harvest platforms infield or sorting lines in the packinghouse. Over the years, a large variety of bin fillers have been developed for infield use and postharvest handling. This article reviews different bin filling technologies in terms of major design features, performance, and throughput, as well as automatic control and safety features. For infield use, bin filler designs have evolved from the early use of conveyors and reciprocating plates to recent adoption of soft pads and foam rollers, to reduce bruising and improve fruit distributions. For postharvest use, tilted bins and conveyors commonly used with early bin fillers have been replaced with hinged trays and vacuum suction cups for fruit transport and cylinder brushes and swingable dividers for bruise prevention. While many types of bin fillers have been developed, few are suitable for infield use because it imposes more constraints than postharvest use. Despite the use of automatic sensing and control in most bin fillers, human assistance is still needed during their operation. There are still major issues with the current bin fillers, such as large size, complexity in design, uneven fruit distributions, and low throughput. Further effort should, therefore, be directed towards the development of high throughput, simple yet reliable, compact and fully automated, or even intelligent bin fillers for infield and postharvest use.

“…However, on the commercial stage, beyond the strawberry harvest robotics, there are no other robots for apple or other fruit harvest, because of their high cost, low efficiency, and fruit bruising issue, the robots are still far from being commercially adopted by growers [10][11][12][13][14][15][16][17] . The recently emerged harvest-assist platform technology, which could increase picking efficiency and alleviate occupational injuries, seems an option to assist with pickers [18,19] . However, since their high cost and bruising issue, harvest platforms are slow in being adopted by growers [19][20][21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

“…The recently emerged harvest-assist platform technology, which could increase picking efficiency and alleviate occupational injuries, seems an option to assist with pickers [18,19] . However, since their high cost and bruising issue, harvest platforms are slow in being adopted by growers [19][20][21][22][23] . Therefore, currently, workers harvest apples, the same as decades ago, by wearing a bucket during the entire harvest process for temporary holding of apples, and when the bucket is full (approximately 20 kg), workers walk to a bin, where they stoop down to release the harvested apples [24] .…”

Section: Introductionmentioning

confidence: 99%

Ergonomic and efficiency analysis of conventional apple harvest process

Wang

International Journal of Agricultural and Biological Engineering

et al. 2019

Self Cite

Apples are still manually harvested by workers using ladders and buckets. Though it is known that manual apple harvest would probably lead to occupational injuries (e.g., back, neck, and shoulder strains), there has been little research that focuses on identifying the awkward activities/postures of pickers during the harvest process. After categorizing apple harvest work into 12 activities, this study used the method of Rapid Upper Limb Assessment (RULA) to identify awkward postures/activities that occurred during apple harvest. Awkward activities confirmed include descending a ladder, dumping apples, picking high and low apples on a ladder as well as on the ground, and moving a ladder, with potential reasons for each awkward activity provided. Meanwhile, it was demonstrated that pickers spent approximately 64% of working time under awkward postures that would lead to occupational diseases. In addition, this study analyzed picker harvest efficiency in terms of general and detail manners. The general mode assessed harvest activity in terms of picking and non-picking, with results showing that pickers averagely spent 76% (±7%) of harvest time in picking apples. Further analysis evaluated picking activities in terms of reaching, detaching, and transporting apples, with results showing that pickers spent averagely 30% (±6%) of time in detaching apples, which is the value time during apple harvest. Furthermore, valuable picking time ratio was obtained as the multiplication of picking time ratio and detaching time ratio. With a valuable ratio of 22% (±5%), it theoretically demonstrated the low harvest efficiency of the traditional harvest method. Since a majority of the awkward activities and the low efficiency were because of the ladders/buckets, using a harvest-assist unit may be a potential solution. Additionally, more efforts should be spent on the development of innovative mechanism to replace worker in placing attached apples to the bucket. Once the time for transporting apples is eliminated, the time for reaching apples is also removed, indicating the harvest efficiency would be improved significantly.