Abstract
Trunk shakers are the most widely used mechanical harvesting machines for harvesting nut trees, including pistachio in California. Improvement of these machines requires a better understanding of the shaking dynamics of the existing trunk shakers during harvest. In this study, the effects of four different shaking patterns on three sizes of pistachio trees of different ages, shapes, and sizes were investigated under field conditions. The vibration acceleration of the real pistachio tree was measured using a wireless network of 3-axis accelerometers installed on the shaker head and different parts of the trees during the shaking harvest. Changes in acceleration and the effect of tree morphology on the magnitude of acceleration in each pattern are presented and discussed. A new location index λ, which is based on the distance of the sensor from the shaking point and diameter of the branch at each sensor location, is introduced. This study focused on mathematical modeling of the variation and distribution of the acceleration throughout the tree canopy. The sensor location index, relative force ratio (RFR), and relative kinetic energy ratio (RKER) were defined to better understand the energy damping in each part of the tree. The results showed that the relationship between the acceleration peaks and the sensor location index could be expressed by a third-degree polynomial function with an acceptable coefficient of determination. Under different shaking patterns, similar changes in the RFR of the tree at different locations and for different trees were observed. This finding indicates that the vibration force is significantly damped as the distance from the shaker clamps increases. However, the RKER values at the same shaking pattern result in different effects at different points on the branches according to tree morphology.
Highlights
Pistachio production has rapidly expanded in the state of California, and the majority of the acreage is located in the San Joaquin Valley, where over 90% of the U.S, pistachio crop is located [1]
Trunk shakers are widely used for harvesting nut trees in California, there is not a sufficient amount of published scientific study about the efficiency of these machines. erefore, a comprehensive study is required to determine the characteristics of the shaking method that these shakers presently use. e methods proposed in this study will provide design engineers with more information about the performance of these harvesting machines under field conditions
Field experiments were conducted at a pistachio orchard at Kern County, which is located in the southwest part of the San Joaquin Valley, California. e data were collected in the second week of October 2018. e C7 R-SERIES MK2 catching frame shaker (Coe Orchard Equipment, Inc., Live Oak, CA, USA), an inertia-type trunk shaker mechanical harvesting machine (Figure 1), was used in this experiment. e harvesting machine was equipped with a computerized shaking pattern controller that allowed the operator to define different shaking patterns by controlling and changing the magnitude of shaking frequency and the duration of shaking
Summary
Pistachio production has rapidly expanded in the state of California, and the majority of the acreage is located in the San Joaquin Valley, where over 90% of the U.S, pistachio crop is located [1]. Mechanical trunk shaker harvesting machines are the primary method of harvesting pistachios in California. Since the 1920s, shake-based harvesting machines have been used in the U.S fruit and nut crops [3, 4], and trunk shakers have been used since the early 1960s [5, 6]. E methods proposed in this study will provide design engineers with more information about the performance of these harvesting machines under field conditions. Trunk shakers are widely used for harvesting nut trees in California, there is not a sufficient amount of published scientific study about the efficiency of these machines. Using this information, they could potentially make modifications to improve the shaking mechanism or shaking pattern to
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