Design Features and Bruise Evaluation of an Apple Harvest and In-Field Presorting Machine

Abstract

Abstract. In-field presorting of apples, in combination with the harvest aid function, would have advantages of cost savings in postharvest handling and storage, reduced postharvest pest and disease problems, and better inventory management, while also enhancing harvest productivity. A new apple harvest and in-field presorting prototype was developed to help apple growers achieve these potential benefits. The prototype sorts and grades fruit based on color and size, using a machine vision-based sorting system with an innovative fruit singulating and rotating design (SRD), and it handles the graded fruit in the bins using newly designed automatic bin fillers. Bruise damage by impact is a critical factor in the development of the apple harvest and in-field presorting prototype. This article reports on the major design features of the prototype and experimental evaluation of the prototype for potential bruise damage. Experiments were conducted on ‘Gala’ and ‘Fuji’ apples to evaluate bruise damage potential under both empty and partially filled bin conditions. An impact recording device (IRD) was used to measure the impact magnitude in terms of peak acceleration (G) at all critical points of the machine, including harvest conveyors, main conveyor, flat conveyor, SRD, cup conveyor, bin filler, and bins. It was found that bruise damage mainly occurred during bin filling. The number of impacts recorded for the partially filled bin was reduced by 60%, compared to that for the empty bin, indicating that the impact between apples and the wooden bin’s floor was a major cause of bruising. The maximum G value for the partially filled bin was measured at 34.5, while the measured G values were less than 20 from start to the point just before the bin filler, indicating no bruise damage. Bruise evaluation showed that no more than 9% of the test apples would be downgraded from ‘Extra Fancy’ grade for the partially filled bin condition. Higher G values for the empty bin condition suggested the need for further improvement to the discharge of apples from the bin filler to the bin to further reduce bruise damage. Keywords: Apples, Bruising, Fruit, Grading, Harvesting, Sorting, Machine vision.

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

“…The in-field assessment of the proportion of fruit grades can be used to inform the in-field consolidation of harvest bins with similar-quality fruit. In another step, Pothula et al [105] report on the development of an in-field pre-sorting machine in which machine vision is employed on a harvest aid to separate fruit by quality into separate field bins.…”

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

“…Chi-square test results of texture features extracted from GLCM of different distance and orientation are shown in Figure 6. It could be seen from the picture that the minimum p-value of Contrast appeared at (3,12). The minimum p-value of dissimilarity appeared at 7 positions, and the first one was (0,3).…”

“…The minimum p-value of ASM appeared at 14 positions, and the first one was (2,3). The minimum p-value of energy appeared at 14 positions, and the first one was (2,3). The minimum p-value of correlation appeared at 36 positions, and the first one was (0,0).…”

“…It takes a lot of labor to plant and harvest apples. With the continuous expansion of planting area and yield, precision planting and mechanized harvesting of apples are urgently needed [2,3]. Yield estimating, robot harvesting, precise spraying are important processes for precise planting [4][5][6][7].…”

Ergonomic and efficiency analysis of conventional apple harvest process

“…Researchers have been working on developing apple harvest robots for decades to fully replace human labor, and actually several prototypes of harvest robots have been developed [2][3][4][5][6][7][8][9] . 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] .…”