Citrus Fruit Movement Assessment Related to Fruit Damage during Harvesting with an Experimental Low-Frequency–High-Amplitude Device

Abstract

The profitability of fresh citrus crops outlines the possibility of using mechanical systems to reduce the high cost of the manual harvesting operation. In this line, an experimental light shaker with low frequency and high amplitude and a hydraulic arm was developed. This device had been successfully used for harvesting ornamental citrus trees. In previous studies, it was proven that the removal percentage was adequate, but the percentage of slightly damaged fruit increased compared to a traditional fruit trunk shaker. Visualizing the fruit movements in high-speed video recording images during harvesting, a higher impact level was observed due to the higher amplitude. The aim of this study was to analyze the fruit movement during the vibration with the experimental shaker compared to the movement when vibrating with an orchard tractor mounted trunk shaker related to the increase of slightly damaged fruit when using the experimental shaker. Fruit movement parameters (fruit distance, fruit displacement, speed and acceleration) when using the two harvesting systems (experimental shaker and trunk shaker) were compared analyzing the high-speed video recordings. Results showed that the higher vibration amplitude produced a maximum distance experienced by the fruits (maximum displacement of 0.135 m with the experimental shaker compared to 0.078 m with the trunk shaker) that could cause a higher damage level (74% of slightly damaged fruits using the experiemntal shaker compared to 14% using the trunk shaker).

Finite Element Simulation of Branch Vibration Model Based on Discrete Elements

“…In order to select the optimal natural frequencies for fruit trees, the frequency at which the acceleration exceeds 120 ms -2 in different branches of the tree is chosen as the first evaluation criterion [4,13,16,17,28]. Typically, the acceleration of fruit detachment falls within the range of 0 to 120 ms -2 .…”

“…So far, vibration harvesting technology has been applied to the harvesting of fruits such as jujubes, citrus fruits, walnuts, pistachios, pecans, and coffee beans [6][7][8][9][10][11][12][13]. Various types of forest fruit vibration harvesting machines have been developed, categorized into crown vibration harvesters and trunk vibration harvesters based on the different vibration modes [6,7,14].…”

“…When a fruit tree is subjected to a vibration stimulus, both the branches and fruits will vibrate. The fruit undergoes pendulum motion relative to the branches, and when the fruit experiences an inertial force due to the magnitude of acceleration and its own mass, the fruit stem will break when the inertial force exceeds the adhesion force of the stem, resulting in fruit detachment and successful harvest [13,[15][16][17][18].…”

Vibration Response of Walnuts under Vibration Harvesting

“…In contrast with only determining the relationship between the vibration response of fruits and a certain vibration parameter, in this study, the relationship was expanded to the joint action of the vibration frequency and amplitude, with the association being established using a specific equation in a more intuitive way. The relationship provides a theoretical explanation for the experimental light shaking at low frequency and high amplitude generating a low response acceleration and high damage level compared with the use of a trunk shaker [31] and suggests that the vibration frequency and amplitude need to be appropriately and effectively combined for vibration harvesting [24,26]. Additionally, the equation provides a reference for the design of the vibration harvesting device, and its use is expected to increase the harvesting rate of walnuts under vibration harvesting and avoid improper parameter design in the mere pursuit of resonance harvesting, which reduces the service life of a harvesting machine and damages trees [19,27].…”

An Improved Rotating Box Detection Model for Litchi Detection in Natural Dense Orchards

“…With the rapid development of deep learning technology, excellent target detection algorithms such as YOLO, SSD, and Faster R-CNN have been applied to fields such as fruit and vegetable detection [14][15][16][17][18][19]. For example, the k-means++ clustering algorithm has been used to optimize bounding box settings, reduce the number of network layers, and improve litchi detection in dense scenes [20].…”