Optimized tuber-lifting velocity model for cassava harvester design

Abstract

The lack of optimized lifting velocity model for cassava tuber lifting results in the shortage of evidence of design of lifting velocity control system and large harvest loss during mechanized harvesting of cassava. First, an optimized velocity model of manually pulling tubers and a velocity model of mechanical lifting tubers were established using physical experiments. And then using the mechanical tuber-lifting velocity model, the mathematical models between coefficients of mechanical tuber-lifting velocity model and cassava harvesting quality were established based on numerical simulation and regression analysis. Moreover, the coefficients were optimized using optimization method and the mechanical optimal tuber-lifting velocity model was obtained. Finally, the optimization results and the mechanical optimal tuber-lifting velocity model were verified by simulation and physical experiment, respectively. The results show that the optimized manual pulling velocity model can be superimposed by a line and a sine curve or a concave downward parabola and a sine curve. The optimal coefficients' combination of mechanical tuber-lifting velocity model is shown as follows: A = 0.056, B = 0.521, C = 0.048, D = 0.086, E = 38.506, and F = 1.165. The mechanical optimal tuber-lifting velocity model's expression is simple and the model is reasonable. The mechanical optimal tuber-lifting velocity model, which is established using physical experiments, simulation method, and optimization technique, has great significance for designing lifting velocity control system.

Embedded Field Stalk Detection Algorithm for Digging–Pulling Cassava Harvester Intelligent Clamping and Pulling Device

“…This kind of harvester can function at the fully mechanized level, with great operating efficiency, but it consumes a lot of power and is poorly adapted to diverse soil types where cassava is cultivated, with less tuber loss and damage in sandy soils and more tuber loss and damage in clayey soils. The third type is the digging-pulling cassava harvester, which includes the cassava root digging-pulling type of harvester in Cuba [5], the cassava harvester developed by the University of Leipzig in Germany [13], and a series of digging-pulling types of cassava harvesters developed by Hainan University [14] and Guangxi University [15], as shown in Figure 1c, among others. This kind of harvester can achieve fully mechanized operation, relatively high operational effectiveness, low power consumption, and excellent soil adaptability.…”

Parameter optimization of double‐blade normal milk processing and mixing performance based on RSM and BP‐GA

“…Dong, Zhu, et al () studied the performance parameters of black tea fermentation using extreme learning machine algorithm. Yang et al () optimized the root‐lifting speed of cassava harvester based on improving spider cluster algorithm. Wang, Dong, Wu, and Fang () optimized corn planting density and fertilization quantity based on BP neural network.…”