Open AccessDesign of Algorithm for Forecasting Agricultural Output Quality Based on Genetic Algorithm-Back Propagation Neural Network
Author(s) -
Yuehui Wang,
Bin Wen,
Xiaofeng Liao,
Lan Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012045
Subject(s) - artificial neural network , agriculture , algorithm , genetic algorithm , quality (philosophy) , computer science , backpropagation , agricultural engineering , sugar , machine learning , engineering , geography , philosophy , biochemistry , archaeology , epistemology , chemistry
Nowadays, agricultural technology is developing rapidly, and the concept of smart agriculture is gradually popularized. Economic development has made consumers pay more attention to the quality of agricultural output, so the prediction of the quality of agricultural output has also become important. This research takes the sugar content level of apples as an example of the quality of agricultural output, combined with the meteorological conditions of each crop period in the apple producing area as an influencing factor, and uses the back propagation neural network optimized by genetic algorithm to establish the quality of agricultural output Algorithm model. The experimental results show that the accuracy of GA-BP predicting apple sugar content grade, MAE and RMSE are 90%, 0.1 and 0.316 respectively, which is more ideal than the traditional method.