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Development of artificial neural network models for seeding performance of a belt-type corn seed metering device
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800371.(doi:10.13031/aim.201800371)
Authors: Qi Niu, Caiyun Lu, Hongwen Li, Qingjie Wang, Hongnan Hu, Zhongcai Wei, Hongbo Zhao
Keywords: Precision agriculture, Artificial Neural Network, Genetic Algorithm, seed metering device
Abstract. Seed planting equipment with mechanical seed metering device is commonly used for corn in China. In order to obtain the high working effective and corn yield, it is important to plant the corn seed in rows maintaining accurate seed rate (single seeds, misses, and multiples) and sowing uniformity when adopting high speed planting. This mainly depends on the forward speed of the seeder, rotary speed of the seed metering device and length of pickup finger. To gain high seed rate and seed spacing pass rate and determine the relationship among these factors and performance parameters, a prediction of the performance parameters (seed rate, seed spacing pass rate) and the optimization of rotary speed, forward speed and length of pickup was developed by using Artificial Neural Network (ANN) and Genetic Algorithms (GA). The performance parameters were collected under the laboratory conditions. The ANN model consisted of three layers with 10 nodes of hidden layers, and predicted data were closely related to measured data. GA model successfully searched the optimum rotary speed, forward speed and length of pickup that were 26.2 r/min, 8.2 km/h and 5.5 mm, respectively. It provided a method to obtain the desired performance parameters and determine the working parameters of belt-type seed metering device by using ANN models and GA model.
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