Crop yield prediction method and system, storage medium and electronic equipment
A crop yield and prediction method technology, applied in the agricultural field, can solve problems such as limited application, numerous calculation parameters, and complex boundary conditions
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2021-07-23
Smart Images

Figure 1 
Figure 2 
Figure 3
Abstract
Description
technical field
[0001] The invention relates to the field of agricultural technology, in particular to a crop yield prediction method, system, storage medium and electronic equipment. Background technique
[0002] Hetao irrigation area is an important grain production base in my country. Due to the sharp decrease in the amount of water diverted from the Yellow River and the imperfect management and utilization of water resources, problems such as secondary soil salinization in irrigated areas and agricultural non-point source pollution have become increasingly serious, restricting the sustainable and healthy development of agriculture in irrigated areas. How to improve saline land, improve efficiency and increase production is the main problem faced by irrigation districts. The distribution of water and salt in saline soil in irrigation areas affects the growth of crops. The study of water and salt migration can provide a theoretical basis for improving efficiency and yield...
Examples
Embodiment Construction
[0045] like figure 1 As shown, a method for predicting crop yield in the embodiment of the present invention comprises the following steps:
[0046] S1. Obtain the specific value of each parameter associated with the growth of the preset crop;
[0047] S2. Input the specific values of all parameters into the progressive water-salt embedding neural network model to obtain the predicted value of the yield of preset crops, wherein the progressive water-salt embedding neural network model is used to simulate all parameters and soil water-salt content The functional relationship between, and the functional relationship between the simulated soil water and salt content and the yield of preset crops.
[0048] The progressive water-salt embedded neural network model includes 2 layers of progressive causality, which is embodied as: using the functional relationship between all parameters and soil water-salt content to obtain the specific value of soil water-salt content, based on th...