Apple Harvesting Research Articles

Developing undergraduate community psychology pedagogy and research practice

In this study two community psychology students describe their experiences in participating in different types of “green” volunteer sustainable programs. In the first scenario, Anthonie Collins describes his experiences in working with young men in a youth detention facility and how the participants discovered the intrinsic value of maintaining a vegetable community garden. In the second scenario, Jordan Seitz describes her experiences in organizing a community apple harvest at a local fruit tree orchard in Wyoming, MN. At the conclusion of their community service projects, participants were interviewed regarding their overall subjective experiences in participating in an outdoor “green space” community service project and how superordinate programs that provide increased positive contact helped improve understanding of persons from ethnically diverse cultures and appreciate the value of outdoor “green” voluntary programs.

Ergonomic and efficiency analysis of conventional apple harvest process

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. Keywords: rapid upper limb assessment, awkward postures, apple harvest, safety, ergonomic analysis, harvest efficiency analysis DOI: 10.25165/j.ijabe.20191202.4567 Citation: Zhang Z, Wang Y J, Zhang Z H, Li D P, Wu Z Z, Bai R, et al. Ergonomic and efficiency analysis of conventional apple harvest process. Int J Agric & Biol Eng, 2019; 12(2): 210–217.

Simulation of Apple Picking Path Planning Based On Artificial Potential Field Method

This paper aims at the problem of picking path design for apple harvesting robot manipulator in dynamic and unstructured environmen, and according to the obstacle avoidance situation and non obstacle avoidance situation, the picking path design is achieved respectively. The non obstacle avoidance path is designed by the default path planning method and the artificial potential field method is used to avoid obstacles. By combining the characteristics of obstacles in the growth environment of fruit trees, the principle of two-dimensional potential application in artificial potential field is transferred to three-dimensional space, and a more smooth obstacle avoidance motion curve is obtained. The proposed picking path planning method is verified by Java graphical interface and MATLAB. The results show that picking path planning based on artificial potential field can effectively avoid obstacles and achieve timely and reliable picking.

A feasibility study comparing objective and subjective field-based physical exposure measurements during apple harvesting with ladders and mobile platforms

ABSTRACTAlthough mobile orchardplatforms have been developed to improve apple harvesting productivity in the US, the physical exposures of workers usingthe mobile platforms have not been well characterized, partlydue to the lack of assessment tools specific to the tree fruitorchard environment. The purpose of this study was to evaluate the feasibility and utility of different subjective and objective methods for characterizing apple harvesting workers’ posture, armrepetition, heart rate, and perceived exertion during platform- and conventional ladder-based harvesting. During a regular full shiftwork (8 hours), the objective physical exposure measures (armelevation, torso inclination, and heart rate) of 6 platform, 6 ground, and 8 ladder workers were measured with tri-axial accelerometersand heart rate monitor; and subjective perceived exertion wascollected using standardized Borg RPE and CR-10 scales, translated into Spanish. The results showed that the arm elevation, torso forward bending, repetitiveness, heart rates, and perceived exertions were lower for the platform-based workers than forthe ladder-based workers. The subjective measures (Borg RPE and Borg CR-10) appeared to be similar and mirror the general trends of the objective heart rate and posture measures.These results indicate the potential benefit of these low-cost subjective measures when direct measurements are too costly,complicated, or not permitted. This study determined that field measurements of objective and subjective physical exposures were feasible for evaluating apple harvesting work. In summary, all themethods used appear to be feasible for field use in orchard-based environments..

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

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 multi-purpose 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. Keywords: multi-purpose apple harvest platform, economic evaluation, infield sorting, pruning, thinning, training DOI: 10.25165/j.ijabe.20191201.4360 Citation: Zhang Z, Zhang Z H, Wang X M, Liu H, Wang Y J, Wang W J. Models for economic evaluation of multi-purpose apple harvest platform and software development. Int J Agric & Biol Eng, 2019; 12(1): 74–83.

Ergonomic and efficiency analysis of conventional apple harvest process

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. Keywords: rapid upper limb assessment, awkward postures, apple harvest, safety, ergonomic analysis, harvest efficiency analysis DOI: 10.25165/j.ijabe.20191202.4567 Citation: Zhang Z, Wang Y J, Zhang Z H, Li D P, Wu Z Z, Bai R, et al. Ergonomic and efficiency analysis of conventional apple harvest process. Int J Agric & Biol Eng, 2019; 12(2): 210–217.

Effect of Shaking Amplitude and Capturing Height on Mechanical Harvesting of Fresh Market Apples

This study evaluated the effect of shaking amplitude and capturing height on mechanical harvesting of fresh market apples in trellis trained trees. A linear-forced limb shaker with adjustable shaking amplitude and frequency was designed and fabricated. Shaking amplitudes of 20, 25, 30, 35 and 40 mm were accessible. A catcher filled with 50 mm thickness of peanut foam underneath a piece of cotton was developed. The shaker and the catcher mounted on a movable lifting platform were integrated into a shake-and-catch harvesting system. The approximate middle of a targeted limb was selected as the shaking point and detached fruits were captured underneath the targeted section. The overall combinations of five levels of shaking amplitude and two levels of capturing height were tested for ‘Pink Lady’ apple trees trellis trained in a vertical fruiting wall architecture. Shaking frequency with 20 Hz and duration with 5 s were used in all tests. Fruit removal efficiency and fruit quality (USDA standard) were adopted to evaluate the quality of the harvesting system. Statistical analysis shows that fruit removal efficiency was significantly improved with increase of amplitude at a certain range; the capturing height significantly affected the percentage of Extra Fancy grade fruit. The results indicated that shaking amplitude with ~30 mm is sufficient to remove majority of fruits in the tested variety; capturing fruits that are much closer to the targeted limb is promising to obtain more Extra Fancy grade fruit.

Improvements and Evaluation of an In-Field Bin Filler for Apple Bruising and Distribution

Abstract. Automatic bin filling is needed for apple harvest and in-field sorting. A commercially viable bin filler for in-field use should be simple, compact, low in cost, and be able to distribute apples evenly in the bin without causing bruising damage. An innovative bin filling technology was developed for incorporation with the new apple harvest and in-field sorting machine recently developed by our group. Field tests of the first version of the bin filler in the 2016 harvest season showed relatively high bruising rates and uneven fruit distributions. Subsequently, a second version of the bin filler was developed with several major improvements. A new pair of foam rollers for better control of apples exiting the sorting system and avoiding fruit collisions during free falling was added below the sorter. An improved pinwheel with nine longer soft pads, instead of four short pads as in the original version, was installed for better fruit distribution. Foam guides, attached to the long pads, reduced the rolling speed of fruit from the pads into the bin. Field tests conducted in the 2017 harvest season showed that the second, improved version of the bin filler achieved superior performance in reducing bruise damage, with 99% of ‘Gala’ apples and 98% ‘Blondee’ of apples graded Extra Fancy. Furthermore, a depth imaging method, using a Kinect-v2 camera, was proposed to quantitatively compare the performance of the two bin fillers for distribution of fruit in the bin under uniform and non-uniform feeding conditions. Analysis of the fruit height data showed that the apple distributions were not significantly affected by feeding method for both bin fillers. Overall, the second version of the bin filler resulted in better distributions of apples in the bin, compared to the first version, and uneven distributions mainly occurred in the corners of the bin, which could not be reached by the bin filler’s pinwheel. The improved bin filler meets the requirements for apple harvest and in-field sorting, and it has potential for use with other harvest platforms. Keywords: 3D imaging, Apple, Bin filling, Bruising, Harvest, Sorting.

Design and Field Evaluation of a Robotic Apple Harvesting System with a 3D-Printed Soft-Robotic End-Effector

Abstract. Fresh market apple harvesting is a difficult task that relies entirely on manual labor. Much research has been done on the development of mechanical harvesting techniques. Several selective harvesting robots have been developed for research studies, but there are no commercially available robotic systems. This article discusses the design and development of a novel pneumatic 3D-printed soft-robotic end-effector to facilitate apple separation. The end-effector was integrated into a robotic system with five degrees of freedom that was designed to simplify the picking sequence and reduce costs compared to previous versions. Apples were successfully harvested using the low-cost robotic system in a commercial orchard during the fall 2017 harvest. A detachment success rate on attempted apples of 67% was achieved, with an average time of 7.3 s per fruit from separation to storage bin. By conducting this study in an orchard where problematic apples were not removed to increase the detachment success rate, current pruning and thinning practices were assessed to help lay the foundation for future studies and develop strategies for successfully harvesting apples that are difficult to detach. Keywords: Apple catching, Apples, Automated harvesting, Field experimentation, Harvesting robot, Soft-robotic gripper.

The Role of a New Harvest Platform in Alleviation of Apple Workers' Occupational Injuries During Harvest.

. Migrant seasonal workers manually harvest apples throughout the U.S. using ladders and buckets due to the care required for prevention of bruises and other physical damage. The motions associated with hand harvest have potential to cause musculoskeletal disorders of the neck, shoulder, and back/trunk. A prototype harvest platform was developed, aimed at alleviating occupational injuries and increasing harvest productivity by replacing ladders for reaching high apples. This study evaluated the activities of three harvest methods, i.e., conventional harvest (using ladders and buckets), harvest platform (for high apples only), and combined method (conventional harvest for low and middle apples and harvest platform for high apples) using the Rapid Upper Limb Assessment (RULA) method performed by trained researchers. Postures/activities with RULA grand scores > 5 were categorized as awkward (causing occupational injuries); otherwise, they were considered comfortable (not leading to health issues). Experimental results demonstrated that awkward activities in conventional harvest were mainly related to the use of ladders. Activities with the harvest platform were comfortable due to the elimination of ladders and buckets. The combined method significantly decreased the workers' time spent in awkward postures (from 64% with conventional harvest to 30% with the combined method) by eliminating awkward activities and increased the overall harvest productivity by approximately 40%. Apple growers and workers are therefore suggested to use the combined method to replace conventional harvest.

Effect of Shaking Location and Duration on Mechanical Harvesting of Fresh Market Apples

Abstract. With adopting the modern high-density tree architectures, localized shake-and-catch harvesting is becoming more promising for fresh market apples. To investigate the shaking inputs for an effective harvesting system, a multi-tier shake-and-catch harvesting platform was developed and evaluated in a commercial orchard. The platform composes of a shaker and two three-tier catching surfaces. Performance test was conducted in ‘Jazz’ apple trees trained to a vertical fruiting wall architecture (two horizontal branches at each tier). Two harvesting sections were defined in the test trees, namely, Section I - two neighboring branches at the same tier from two adjacent trees; and Section II - two branches at the same tier of a tree. For Section I, shaking was applied to the middle of each branch (location P1), and for Section II, shaking was applied at the location close to the trunk (location P2). Shaking frequency was set to 20 Hz, and two shaking durations (i.e., 5 and 2 s) were used. In the test, the catching angle relative to the horizontal plate was set to 15°. The results indicated that the fruit removal efficiencies (2nd to 4th tiers of trees were used in the analysis) were 76.6% and 78.1% for shaking at middle of each branch in 2 and 5 s shaking, respectively; those numbers were 86.3% and 89.5% for shaking at location P2. The percentage of marketable fruits were 89.7% and 90.6% for the Section I shaking in two durations, and 84.5% and 84.4% in the Section II shaking. Overall, this study revealed that 2 s shaking duration is sufficient to remove majority of fruits in the tested variety, and the section II shaking had higher overall fruit removal efficiency but also caused slightly more fruit damage. Keywords: Fresh market apples, Fruit quality, Fruit removal efficiency, Mechanical harvesting, Multi-tier shake-and-catch.

Canopy Segmentation Using ResNet for Mechanical Harvesting of Apples

Fresh market apple is the number one premium fruit crop in Washington state, accounting for more than 60% of U.S. national production every year. With the rapid increment of agricultural labor cost and the decrement of labor availability, mechanical apple harvesting is considered as an alternative solution. To further improve the efficiency of the harvester by automatically locating the target tree trunk and/or branch, the tree canopy under the full foliage condition needs to be analyzed. In this study, convolutional neural network (CNN)-based semantic segmentation method was adopted to segment the tree canopy using a pre-trained and modified ResNet-18 implementation of CNN. In total, 253 images were acquired using a Kinect V2 camera in a commercial “Fuji” apple orchard (trained in the formal architecture with V-axis) during 2018 harvest season near Prosser, Washington. Among those images, 152 (60%), 51 (20%), and 50 (20%) were used for network training, validating, and testing, respectively. Three different classes of pixels were defined including ‘trunk/branch’, ‘apples’, and ‘leaves’ (background) for each image. Then, three commonly adopted evaluation measures were employed to examine the performance of the model; i) normalized confusion matrix (per-class accuracy), ii) intersection over union (IoU), and iii) boundary-F1 score (BFScore) on the image basis. Test results showed that all three classes achieved reasonably high per-class accuracies of 94.8% (trunk/branch), 97.5% (apples), and 94.5% (leaves), respectively. In addition, IoUs for each class in the same sequence were 0.408, 0.717, and 0.944 whereas BFScore for each class were 0.761, 0.915, and 0.887, respectively. Among all the three classes, a generally poor result was achieved for ‘trunk/branch’ compared to the same for other two classes, which was primarily due to proportionally smaller number of pixels in each image. The results from this study indicated that the efficacy of the mechanical harvesting technique of apples could be potentially improved by automatically locating and shaking the trunk/branch under full foliage canopy during the harvest seasons.

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

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

Measurement of apple harvesting behavior applying estimation error reduction method from body-mounted inertial sensor by low pass filter

Measurement of apple harvesting behavior applying estimation error reduction method from body-mounted inertial sensor by low pass filter

Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN)

Due to the rising cost and decreasing availability of labor, manual picking is becoming an increasing challenge for apple growers. A targeted shake-and-catch apple harvesting technique is being developed at Washington State University to address this challenge. The performance and productivity of such a harvesting technique can be increased greatly if the shaking process is automated. The first step toward automated shaking is the detection and localization of branches in apple tree canopies. A branch detection method was developed in this work for apple trees trained in a formal, fruiting wall architecture using depth features and a Regions-Convolutional Neural Network (R-CNN). Microsoft Kinect v2 was used to acquire RGB images and pseudo-color images, as well as depth images in natural orchard environment. The R-CNN was composed of an improved AlexNet network and was trained to detect apple tree branches using integrated pseudo-color and depth images for improved detection accuracy. The average recall and accuracy from the Pseudo-Color Image and Depth (PCI-D) method were 92% and 86% respectively when the R-CNN confidence level of the pseudo-color image was 50%. For comparison, when using the Pseudo-Color Image (PCI) method (without depth images), these averages were only 86% and 81%, respectively. Furthermore, the average correlation coefficient (r) between the fitting curves for branch skeletons using the PCI-D method and the fitting curves for ground-truth images was 0.91—another indicator that the PCI-D method performs better than the PCI method. In addition, the average accuracy of branch detection increased with both the PCI method and PCI-D method, since the sensor was closer to the canopy. This study demonstrates the great potential for using depth features in branch detection and skeleton estimation to develop effective shake-and-catch apple harvesting machines for use in formally trained apple orchards.

Pemodelan Fuzzy Inference System Tsukamoto Untuk Prediksi Curah Hujan Studi Kasus Kota Batu

The uncertain pattern of rainfall causes apple farmers to be difficult in determining the flowering time that resulted in the apple harvest becomes not maximal. Many methods are used to predict rainfall, one of which is Fuzzy Inference System (FIS) Tsukamoto. Earlier research using this method managed to get a fairly small Root Mean Square Error (RMSE) value. In this research, FIS Tsukamoto method is used to create rainfall prediction modeling in four locations in Batu, East Java with the aim to get a small RMSE also. Tsukamoto's FIS method is used to predict rainfall with time series data from 2005 to 2014. The result of this research is the prototype of Tsukamoto FIS method that can be used to predict rainfall with RMSE error value in Junggo area of 9,196, in Pujon area of 9,407 , in Tinjomulyo area of 8,798, in Ngujung area of 8,825.

APPLE EXPORT FROM POLAND IN THE YEARS 1995-2015

The aim of the research was to determine the factor affecting the volume of apple export from Poland in the years 1995-2015. The studies used a linear regression model. Research has shown that the most important factors determining the volume of apple exports from Poland were: the volume of Russian apple import, apples harvest in Poland, the exchange rate of PLN/USD and producer prices in the previous year. The increase in import demand for apples in Russia by 1 thousand tons caused, on average, a rise in export from Poland by 585 tons. The increase in apple harvest in Poland by 1 thousand tons resulted in an increase in export by 292 tons. The zloty depreciation and higher producer prices in previous years also had a positive impact on the volume of apple export from Poland.

Understanding musculoskeletal disorders among Iranian apple harvesting laborers: Ergonomic and stop watch time studies

BackgroundMusculoskeletal disorders (MSDs) are usually caused by bad working postures and habits. There is a great demand to correctly estimate laborers' work-related musculoskeletal disorders (WMSDs) during physically intensive works. This study aimed to suggest a practical solution with ergonomic principles and time studies on the reduction of WMSDs for the apple harvesting laborers in Sepidan gardens at the Fars Province, Islamic Republic of Iran (Iran). MethodsPrevalence of MSDs was evaluated by the Nordic Standard Questionnaire surveys and the laborers’ workload was assessed by the rapid entire body assessment postural analysis tool. A stop watch time study method was employed to estimate rest time allowances. Data from the time study was also recorded by a questionnaire validated from a panel of experts. Thirty laborers from two age groups: a young (20–35 years of old) and old (36–55 years old) were sampled from 10 gardens. ResultsThe stop watch time study revealed that the frequencies of apple harvesting works were 95 vs. 119 times for the old group and young one, respectively. The prevalent disorders were related to specific body regions such as the lumber, knee, neck and shoulder areas. The percentages of disorders were significantly reduced when ergonomically corrected postures were applied with suitable rest time allowances. ConclusionFruit harvesting works may need to improve their work-rest time intervals to prevent WMSD developments and productivities with time managements. With a correct estimation of the desired number of laborers, apple harvesting jobs can be performed on time, and by implementing appropriate ergonomic postures, occupational health and safety problems can be lessened in the apple harvesting workers.

Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots, there is still a long way to go for commercialization. The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots, this study focused on the extended operating time and proposed a round-the-clock operation mode. Due to the influences of light, temperature, humidity, etc., the working environment at night is relatively complex, and thus restricts the operating efficiency of the apple harvesting robot. Three different artificial light sources (incandescent lamp, fluorescent lamp, and LED lights) were selected for auxiliary light according to certain rules so that the apple night vision images could be captured. In addition, by color analysis, night and natural light images were compared to find out the color characteristics of the night vision images, and intuitive visual and difference image methods were used to analyze the noise characteristics. The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night, and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise. The preprocessing method can provide a theoretical and technical reference for subsequent image processing. Keywords: apple harvesting robot, night vision image, preprocessing method, color analysis, noise analysis DOI: 10.25165/j.ijabe.20181102.2822 Citation: Jia W K, Zheng Y J, Zhao D A, Yin X, Liu X Y, Du R C. Preprocessing method of night vision image application in apple harvesting robot. Int J Agric & Biol Eng, 2018; 11(2): 158–163.

Storage performance of two ‘Pink Lady ® ’ clones differs, but 1-MCP treatment is beneficial, regardless of maturity at harvest

Abstract Determining optimal apple harvest time is important for effective postharvest treatments and also for maintenance of quality along the supply chain. A non-destructive instrument, the DA meter, was used to measure changes in absorbance near the upper chlorophyll-a absorption peak and to segregate commercially harvested fruit into two ripeness levels. DA meter readings were made at harvest and following cold (regular air) storage removals for two ‘Pink Lady®’ clones, ‘Cripps Pink’ and ‘Rosy Glow’. In addition, the destructive maturity and/or quality indicators of starch pattern index, fruit firmness, and soluble solids were determined and ethylene and CO2 production were monitored at harvest and at each removal, i.e. every 1.5 months for 7.5 months, and during a 14 d simulated shelf life per removal. The DA meter readings were a reliable non-destructive technology for determining ripeness levels throughout storage. Ethylene production patterns were similar up to 4.5 months of cold storage for the two cultivars, ‘Cripps Pink’ and ‘Rosy Glow’, but the rate was approximately double in ‘Rosy Glow’. Treatment with 1-methylcyclopropene (1-MCP) was effective at inhibiting ethylene evolution during cold storage and simulated shelf life, regardless of harvest maturity in both cultivars. However, this benefit only lasted until 4.5 months of cold storage for ‘Rosy Glow’, when ethylene production increased markedly after 7 d of simulated shelf life. The storage disorders, superficial scald and internal browning were more evident in ‘Rosy Glow’ fruit than in ‘Cripps Pink’, particularly after 6 months of cold storage. It is recommended that ‘Cripps Pink’ and its clone, ‘Rosy Glow’, be kept separately in storage and that ‘Rosy Glow’ not be stored for as long as ‘Cripps Pink’. Alternatively, the efficacy of additional 1-MCP treatments after 4 months of cold storage could be explored in ‘Rosy Glow’ to maintain reduced ethylene production during longer storage.