Leaf vegetable yield prediction method

Abstract

The invention discloses a leaf vegetable yield prediction method which is aimed at overcoming a problem of limitation in a conventional yield in-vitro prediction and estimation method. The leaf vegetable yield prediction method comprises the following steps: in a first step, basic parameters are obtained; in a second step, data is stored and processed; in a third step, yield per plant is measured: (1) leaf mass prediction is carried out, and (2) yield per plant of lettuce is predicted via an equation m plant=11.297m leaf+48.827; in a fourth step, yield per area is measured: (1) ten plants of lettuce are chosen for measurement and yield prediction in an area being tested via use of a yield prediction model for a single plant of lettuce, and average mass per plant of lettuce in the area is obtained and used as an average yield value in the area being tested; (2) the average mass per plant of lettuce in the area is multiplied by the calculated number of lettuce plants in a lettuce image collected in the area, and an obtained product is lettuce yield data in the area; in an equation M=m average*N, M represents total yield of lettuce in the area, m average represents the average mass per plant of lettuce, and N represents the number of the lettuce plants in the area being tested.

Description

technical field [0001] The invention relates to a method in the field of quality detection of leafy vegetables, more precisely, the invention relates to a method for predicting the output of leafy vegetables. Background technique [0002] Vegetables are an essential food for people's daily life. At present, the demand for vegetables is increasing year by year. In 2014, the annual output of vegetables in my country was 760.05 million tons, ranking first in the world. However, in some areas, due to the influence of seasonal and regional factors, there are problems such as imbalance between supply and demand in the market. Mass production of vegetables can effectively alleviate the contradiction between supply and demand in the region. As of 2015, the total area of ​​greenhouses in my country has reached 4.109 million hectares. Compared with 2009, the total area has increased by 210.9%, accounting for 85% of the world's facility agriculture area, my country's greenhouse area ha...

AI Technical Summary

Problems solved by technology

[0003] At present, the prediction methods for the quality and yield of crops, fruits and vegetables mainly focus on methods such as image recognition, gray scale prediction, regression modeling, and planting experience estimation. There are certain limitations. The shapes and types of greenhouse vegetables are different, and the folds of crop leaves or the growth of plants will inevitably form occlusions and other factors that affect the accuracy of image recognition and collection, such as cabbage, lettuce, radish, etc.; crop growth The amount of data at the sampling points is large, which requires a powerful data mining algorithm, and the grayscale prediction can only be made for small sample data, and the prediction ability is limited; although the method of relying on the experience of growers or experts to estimate the yield has certain accuracy However, it is of great significance to crop production process management such as water and fertilizer control, precise market supply, etc.

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