Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network

Abstract

To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity was predicted using a neural-network-based model by using the Box–Behnken design (BBD) test. According to the extracted results, the neural network model has a good prediction ability, with the determination coefficient of the model and the mean relative error reaching 0.99 and 2.29%, respectively. The impact of the fertilizer discharge parameter combinations on the discharging performances was examined from both macroscopic and microscopic perspectives. During the fertilizer discharge process, the openness formed between the spiral blades and fertilizer outlet presented periodic changes with the continuous rotation of the spiral blade, thus resulting in the uneven discharge of the fertilizer particles. In addition, there are interacting force chains among fertilizer particles, which are not broken in time during the fertilizer discharge procedure, thus resulting in uneven fertilizer discharge. With comprehensive consideration of the fertilizer discharge efficiency, the fertilizer discharge effect, and CV of the fertilizer discharge uniformity, the spiral parameter combination of the fertilizer discharge after neural network optimization are as follows: rotating speed of 47.6 rpm, blade diameter of 90 mm, pitch of 60 mm, and CV of fertilizer discharge uniformity of 19.05%. Under this optimal spiral parameter combination, the fertilizer discharge effect and discharge efficiency were considered to be relatively good. Our work provides references for the design optimization of the spiral fertilizer applicator and fertilizer discharge parameter combination.

Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers

“…The working principle of spiral fertilizer dischargers is as follows: the spiral blades rotate around the axis, and under the pushing of the blades, the fertilizer in the fertilizer box is transported to the fertilizer discharge port and discharged. Its structure is shown in Figure 1 [7]. This fertilizer discharger has low requirements for the physical properties of fertilizers and can be flexibly used for the application of granular fertilizers and powdered fertilizers [8].…”

Design and Optimization of Sugarcane Spiral Fertilizer Applicator Based on Response Surface Methodology and Artificial Neural Networks

“…When the rotational speed reaches a certain limit, the centrifugal force acting on the fertilizer particles will be large, causing them to be ejected outward, which hinders the proper functioning of the fertilizer applicator. Therefore, it is necessary to limit the rotational speed of the discharge blade [22]. By using the empirical Formula ( 7), the maximum rotational speed is determined to be 100.26 r/min.…”

“…Moreover, they exhibit robustness to minor perturbations, adapting seamlessly to input variations and noise [18]. Currently, artificial neural network models are mainly used for the optimization of food and bioprocess parameters and have not been widely used in the optimization of structural parameters [19,20], but there are a few studies on the prediction of fertilizer discharge performance of fertilizer applicator [21,22]. Bu et al [23] used EDEM simulation software to investigate how the rotational velocity and aperture degree of the fertilizer discharge wheel affect fertilizer application performance.…”

Prediction of Tool Eccentricity Effects on the Mechanical Properties of Friction Stir Welded AA5754-H24 Aluminum Alloy Using ANN Model

“…Artificial neural network (ANN) is a technology used in the field of Artificial Intelligence that represents an interconnected network consisting of multi-layered nodes (neurons), including input and output nodes and weighted connections that are meant to simulate a biological neural network [ 18 , 19 ]. Through years of experiments and research, ANN techniques have demonstrated robust learning capabilities and high prediction accuracy through its ability to identify complex mathematical relationships between input and output variables [ 20 ]. Accordingly, ANN models have been widely used in the literature as predictive techniques in a range of different fields, including healthcare [ 21 , 22 ], sustainable development [ 18 , 19 , 23 ], agriculture [ 23 , 24 ], and material science [ 25 , 26 , 27 , 28 ].…”