Irrigation method and device based on machine learning

Abstract

The invention relates to the technical field of intelligent irrigation, and provides an irrigation method and device based on machine learning, and the method comprises the steps: reading environmentdata which comprises weather, soil humidity and plant growth condition data; performing feature engineering processing on the environment data to obtain prediction variable data; sending the prediction variable data to an irrigation effect prediction model, the irrigation effect prediction model being obtained based on labeled historical data training, the label being generated by using a manual method or a machine learning method; obtaining a target variable value output by the irrigation effect prediction model, and generating an irrigation scheme by utilizing the target variable value; andsending a control signal to the water outlet valve with the specified number according to the irrigation scheme. The irrigation opportunity is modeled and controlled based on plant growth conditions and environmental factors, the problem that irrigation cannot be carried out at the optimal opportunity through manual decision making or decision making based on a single environmental factor can be solved, the crop yield can be ensured, and labor and water resources are saved.

Description

technical field [0001] The invention relates to the technical field of intelligent irrigation, in particular to an irrigation method and device based on machine learning. Background technique [0002] With the gradual increase of labor costs and the increasing shortage of fresh water resources, smart irrigation technology has become an urgent need for industries such as agriculture, forestry and gardening. Intelligent irrigation technology uses intelligent equipment to irrigate farmland, gardens, garden plots, greenhouses, etc., which can not only reduce the workload of irrigation, but also save a lot of water resources and reduce irrigation costs as a whole. CN201610889186.X "A Water-Saving Intelligent Irrigation System for Irrigated Areas" discloses an intelligent irrigation system that uses soil moisture as a signal to control whether to irrigate. When the soil humidity is lower than the threshold, irrigation starts; when the soil humidity is higher than When the thresho...

AI Technical Summary

Problems solved by technology

Existing data acquisition technology can ensure the collection of rich site environmental data, but is limited by the data analysis ability of manual judgment or simple computer method judgment, and the existing technology can only judge whether it is necessary based on single factors such as soil moisture or crop images. Irrigate, and use indirect indicators to judge whether plants need to be irrigated. In fact, when to irrigate is affected by various factors such as weather, soil, and small-scale plant growth.

The way of judging the timing and amount of irrigation in the existing technology deviates from the ultimate goal of irrigation, it is difficult to guarantee irrigation at the best time, and the best time for irrigation may be missed, resulting in reduced crop yield or reduced quality of horticultural crops; in reality, there are Complex site types such as farmland, gardens, gardens, or greenhouses where a variety of plants are mixed, the data collected in these scenarios will be more complex, and intelligent irrigation decisions will be more complex. Using existing technologies, it is difficult to determine the timing of irrigation with a single factor. Guarantee the effect of irrigation, while manual decision-making brings heavy work and it is difficult to ensure that each plant in a complex site receives optimal irrigation

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