Analysis of shaking-induced cherry fruit motion and damage

Zhou, Jianfeng; He, Long; Karkee, Manoj; Zhang, Qin

doi:10.1016/j.biosystemseng.2016.02.007

Cited by 47 publications

(24 citation statements)

References 22 publications

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“…FDF reduction was greater when stalk twisting was higher, which could play an important role in fruit detachment process. Although in other crops twisting movement pattern has limited importance (Zhou et al, 2016), in olive inertial and bending forces can be key factors in fruit detachment process (Tsatsarelis, 1987). Therefore, current and future harvesters could take advantage of torsion strain at stalk level, due to the effect on FDF decrease.…”

Section: Discussionmentioning

confidence: 99%

“…In trees subject to forced vibrations produced by trunk shakers, fruit experienced stalk twisting angles under 70 °, with peak angles around 150 º (CastilloRuiz et al, 2016). Moreover during cherry harvesting using limb shaker, twisting has limited influence on the number of motion patterns in comparison with tilting or beam column motion patterns (Zhou et al, 2016). Furthermore, some test and simulations performed in oranges harvested by a canopy shaker determine that only 18% of FDF was applied to fruit stalk suggesting that fruit undergoes twisting and bending processes during mechanical harvesting (Savary et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Olive fruit detachment force against pulling and torsional stress

Castillo-Ruiz¹,

Tombesi²,

Farinelli³

2018

Span. j. agric. res.

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Olive harvesting often requires high hand labour, considering that workers, with long poles or hand held devices, aid trunk shaker due to low harvesting efficiency. Currently, fruit detachment force (FDF) and fruit fresh weight were used to predict harvesting efficiency, although during harvesting process, fruit is subjected to bending and twisting movement besides pulling forces simulated by FDF measurements. For these reasons, the aim of the present study was to determine FDF evolution under different stalk twisting angles. In order to provide more information about mechanical behaviour of olive stalk, a trial was carried out during ripening process on four olive (Olea europaea L.) cultivars: Frantoio, Arbequina, Leccino and Maurino. FDF under traction force was measured after applying different stalk twisting angles (0°, 90°, 180°, 270°, 360°, 540°, 720º). FDF was considered to be 0 when fruit was detached from the bearing branch during the twisting process. Fruit weight, firmness, ripeness index and oil content were also measured to determine the optimal period for olive harvesting and olive ripening stage at each sampling date. FDF was significantly reduced, usually over 180º, when stalk was rotated before applying the pull force to measure FDF, keeping differences along fruit ripening process. Moreover, stalk twisting was an important variable for olive detachment, considering that fruits detached without pulling forces varied between 10.7 and 58.8% of the total fruits according with the different sampling dates. For these reasons, present and future harvesting systems, should take advantage of stalk susceptibility against torsion or bending strain to increase harvesting efficiency.Additional keywords: fruit retention force; olive harvesting; stalk twisting; stalk spinning; fruit rotation; trunk shaker; fruit physics. Abbreviations used: DOY (day of year according to National Snow and Data Center -NSIDC-, from 1 to 365 or 366 in a leap year); FDF (fruit detachment force, this value represents the pulling force at which fruit was detached from bearing branches); FW (fruit fresh weight).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Olive fruit detachment force against pulling and torsional stress

Castillo-Ruiz¹,

Tombesi²,

Farinelli³

2018

Span. j. agric. res.

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“…After getting the space distance between two trajectories, the similarity degree of two trajectories is defined. In fact, Levenshtein distance only considers the absolute factor such as those edges with difference of the two trajectories, but does not take into account the relative factor such as length of motion trajectory (Zhou et al, 2016, Wolz et al, 2018. For example, the space distance between two pairs of trajectories is 5, but one pair is 50 and the other pair is 20.…”

Section: Optimization Of Prediction For Motion Trajectory Track Of Frmentioning

confidence: 99%

RBF network based motion trajectory optimization for robot used in agricultural activities

Sun

2018

Emir J Food Agric

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At present, the efficiency of the method to track and predict motion trajectory of fruit and vegetable picking robot was low and the realization process was complex. Therefore, a research on motion trajectory optimization of fruit and vegetable picking robot based on RBF network was proposed. After analyzing the reason for data class imbalance of fruit and vegetable picking robot, this paper introduced the processing technology MWMO in RBF network. Then, the MWMO technology was embedded in the tracking and prediction research of motion trajectory optimization of fruit and vegetable picking robot. Moreover, the semi-supervised learning algorithm was used as the framework and integrated the processing technology of data class imbalance of motion trajectory to improve the efficiency of tracking and prediction of fruit and vegetable picking robot. According to the integration result, combined with the idea about the calculation of spatial function and the tracking and prediction of motion trajectory in RBF network, we designed the matching principle of trajectory similarity of time and space and realized the matching between the predicted position and the actual position, so that the tracking and prediction of fruit and vegetable picking robot could be completed. Experimental results show that the average calculation time of proposed method is 2.0S, which is only half of average time of traditional tracking and prediction method. It fully proves that the proposed optimization method can accurately track and predict the motion trajectory of fruit and vegetable picking robot. The prediction efficiency is higher and the time consumptionis shorter.

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“…In particular, canopy volume and density appear to play an important role in trunk shaker harvesting effectiveness (Visco, Molfese, Cipolletti, Corradetti, & Tombesi, 2008). Fruit detachment is mainly caused by three oscillation modes; the pendulum, the tilting, and the rotational (or twisting) and longitudinal (or beam-column) (Fridley & Yung, 1975;Tsatsarelis, 1987;Zhou, Long, Manoj, and Zhang, 2016). However, according to Newton's second law of motion (i.e.…”

Section: Introductionmentioning

confidence: 98%