What did we do
Optimization of crop planting density
We have selected six classic yield density models. After you input the yield density data, we will use genetic algorithm and six models to fit the data respectively, and select the best yield density model
Optimum planting density range
We have considered the production cost of crop production. After you input the data, we will calculate the economic benefit yield model, and calculate the optimal planting density range when the economic benefit yield reaches the maximum through graphical method
Optimization of fertilization and planting density
We use BP neural network and polynomial regression to fit the input data, and use genetic algorithm to find the best combination of planting density and fertilizer amount
Data visualization
We also realized data visualization
Which users does the system target
Agricultural researchers
Help agricultural researchers to analyze and model yield density data
Agricultural production personnel
Help agricultural production personnel to make planting density decision
Background and purpose of the system
System design background
Among the factors that affect grain yield, planting density and fertilizer rate are the two most important factors. Since the 1950s, many agricultural researchers at home and abroad have been devoted to the relationship between planting density and crop yield, and have obtained a series of widely recognized yield density models. However, because the relationship between yield and density is mostly nonlinear, and the nonlinear model is more complex, it is difficult to obtain accurate parameter estimation. At the same time, due to the limitation of computer computing power and the cost of using computer at that time, the parameter estimation of yield density model is difficult and the error is large, which limits the promotion of excellent research results to a certain extent
and
System design purpose
By means of modern computing technology and network information technology, the classical yield density equation is optimized, so that the classical yield density equation with large parameter estimation error due to backward computing conditions is more widely used, so as to optimize and promote the excellent research results. The system can not only help the experimenters to analyze the experimental data, but also provide support for the decision-making of planting density and fertilizer amount of agricultural production personnel, so as to avoid the problems of agricultural production personnel setting planting density according to experience, or blindly increasing planting density and fertilizer amount to increase yield, resulting in the decline of yield, the increase of planting cost and even environmental pollution, To guide agricultural production, promote fine agricultural production management and other purposes