Economic Analysis of Robotic Citrus Harvesting in Florida

Harrell, R. C.

doi:10.13031/2013.31943

Cited by 33 publications

(18 citation statements)

References 3 publications

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“…The cycle time achieved was a factor of 12 too long (124 s) and clearly shows that a gap must be bridged. For orange harvesting, comparing cycle time was possible for only one project: 3 s required (Harrell, ) vs 3–7 s achieved (Harrell et al., ), i.e., a factor of about 2 too long. Although this gap is smaller, all performance indicators are required for a more conclusive analysis.…”

Section: Discussionmentioning

confidence: 99%

Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead

Bac

Henten

Hemming

et al. 2014

Journal of Field Robotics

492

351

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This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: (1) simplifying the task, (2) enhancing the robot, (3) defining requirements and measuring performance, and (4) considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops. C 2014 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead

Bac

Henten

Hemming

et al. 2014

Journal of Field Robotics

492

351

View full text Add to dashboard Cite

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“…Since a particular hue and saturation identifies the color orange, it is convenient to represent the colors in terms of just these two quantities (Gonzalez and Woods 1992). In Slaughter and Harrell (1987), the hue and saturation were used to identify individual oranges.…”

Section: Color Clusteringmentioning

confidence: 99%

“…For this reason several projects are being carried out worldwide (Juste and Sevila 1991;Harrell 1987;Molto et al 1992;Sarig 1993;Edan 1995;Kondo et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Localization of spherical fruits for robotic harvesting

Plebe

Grasso

2001

Machine Vision and Applications

113

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The orange picking robot (OPR) is a project for developing a robot that is able to harvest oranges automatically. One of the key tasks in this robotic application is to identify the fruit and to measure its location in three dimensions. This should be performed using image processing techniques which must be sufficiently robust to cope with variations in lighting conditions and a changing environment. This paper describes the image processing system developed so far to guide automatic harvesting of oranges, which here has been integrated in the first complete full-scale prototype OPR.

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“…The analysis of expenses using mechanized work in robotic citrus harvesting was dealt by Harrell (1987). As a result, the most important parameters affecting the cost of harvesting were identified: the inefficiency of the harvesting of the fruit, followed by the price of the harvester, the average cycle time for harvesting the fruit and the repair costs of harvester.…”

mentioning

confidence: 99%

Sensitivity analysis of key operating parameters of combine harvesters

Kavka¹,

Mimra²,

Kumhála³

2016

Res. Agric. Eng.

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Kavka M., Mimra M., Kumhála F. (2016): Sensitivity analysis of key operating parameters of combine harvesters. Res. Agr. Eng., 62: 113-121.The sensitivity analysis of key operating parameters on the average annual sub-profit in a group of three combine harvesters operating in companies providing agricultural services were analysed. Based on the results of the cost analysis, the following key operating parameters with the greatest influence on the costs were identified: the purchase price of the machine, the price of fuel, maintenance costs, personnel costs and annual performance. These parameters were used in the sensitivity analysis to investigate their effect on unit costs. Changing the above-mentioned parameters is calculated within ± 30% from their mean value. To perform a sensitivity analysis of the average annual sub-profit of combine harvesters, the unit price of mechanized work was additionally used. The results showed that greatest impact on both the average annual earnings of combines operation and on the changes in unit cost was those of the annual performance of the combine harvester, combine harvester purchase price and the cost of fuel. On the other hand, maintenance and personnel costs had a smaller influence concerning these changes of parameters.Keywords: annual performance; costs optimization; unit costs; cost analysis; average annual sub-profitThe aim of the sensitivity analysis is to evaluate the impact of changes in key operational parameters for the planned result. Appropriate choice of key parameters has a great impact on achieving successful results. Sensitivity analysis aims at assessing the average annual sub-profit of harvesters as a result of changes in the values of key operating parameters or as a result of others factors, e.g. fuel prices on world markets. It is advisable to perform a sensitivity analysis already in the design stage of the project. Its implementation will identify potential risks and identify key operational parameters influencing profitability.According to Rataj (2005), the implementation of sensitivity analysis is necessary to determine the most probable size of the deviations for both optimistic and pessimistic trends. The estimates are based on the development of these parameters in the past. It identifies the impact of parameter changes on the size of the output (or a project result). As reported by Fotr and Kislingerova (2009), the experience of business practice and empirical research findings show that optimistic distortions dominate strategic financial plans and the resulting values of cash flow.

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Economic Analysis of Robotic Citrus Harvesting in Florida

Cited by 33 publications

References 3 publications

Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead

Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead

Localization of spherical fruits for robotic harvesting

Sensitivity analysis of key operating parameters of combine harvesters

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