Crop growth condition detection method, system and device and storage medium

Abstract

The invention discloses a crop growth condition detection method, system and device and a storage medium. The method comprises the following steps of acquiring a first thermal imaging picture and growth environment data of a to-be-detected crop, determining growth stage information and first leaf surface temperature of the to-be-detected crop according to the first thermal imaging picture, determining a first water stress index of the to-be-detected crop according to the first leaf surface temperature and the growth environment data, and further determining the growth condition of the to-be-detected crop according to the growth stage information and the first water stress index. Crop samples do not need to be collected, crop leaves and other parts do not need to be contacted, related data can be rapidly obtained through an infrared thermal imaging technology and a sensor technology, and the efficiency of crop growth condition detection is improved; the growth condition of the crop is detected by determining the water stress index of the crop, so that the influence of environment and shooting conditions is avoided, and the detection accuracy of the growth condition of the crop is improved. The method can be widely applied to the technical field of agricultural condition monitoring.

Description

technical field [0001] The invention relates to the technical field of agricultural monitoring, in particular to a method, system, device and storage medium for detecting the growth status of crops. Background technique [0002] At present, the detection devices for the growth status of crops include traditional plant growth information detectors, chlorophyll meters and plant nutrition analyzers. Among them, the plant growth information detector needs to extract samples from the plants, use chemicals and instruments for chemical analysis, and obtain the contents of nitrogen, phosphorus, and potassium in the plants, so as to judge the growth status of the plants. The operation is very complicated; although the chlorophyll meter It can accurately measure the chlorophyll content of crops, but it needs to clamp the leaves of the plants with an instrument when using it, which is not conducive to automatic measurement, and cannot perform real-time detection; the plant nutrition an...

AI Technical Summary

Problems solved by technology

Among them, the plant growth information detector needs to extract samples from the plants, use chemicals and instruments for chemical analysis, and obtain the contents of nitrogen, phosphorus, and potassium in the plants, so as to judge the growth status of the plants. The operation is very complicated; although the chlorophyll meter It can accurately measure the chlorophyll content of crops, but it needs to clamp the leaves of the plants with an instrument when using it, which is not conducive to automatic measurement, and cannot perform real-time detection; the plant nutrition analyzer is similar to the chlorophyll meter, which can simultaneously measure the plant chlorophyll content, nitrogen element content and moisture content, etc., but it is also necessary to use the instrument to clamp the leaves of the crops for measurement

[0003] It can be seen from the above that the detection process of the traditional plant growth information detector is complicated, while both the chlorophyll meter and the plant nutrition meter require manual hand-held operation, and the leaves need to be clamped by the instrument for measurement, which cannot be automatically detected, and the efficiency is low

Therefore, the traditional detection method of crop growth status is generally used in scientific research, and it is difficult to promote it in agricultural production.

[0004] A method for detecting the growth status of crops is disclosed in the related art, which judges the growth status of crops by identifying RGB values ​​and texture features of crop images. This method is not only limited by the resolution of crop images, but also affected by weather, Conditions such as light intensity have a great influence, and the detection results are not accurate

Images