A diagnostic method for wheat growth status based on mobile internet patterns and leaf color analysis

Abstract

The invention discloses a wheat growing status diagnosing method based on a mobile internet mode and leaf color analysis. The method is characterized by comprising the following steps that an intelligent mobile phone collects wheat leaf images and sends the wheat leaf images to a background server, the background server removes illumination effects on the wheat leaf images to achieve pre-processed sample images, wheat growing status information and disease results can be achieved from the pre-processed sample images through a color analyzing method and are fed back to the intelligent mobile phone, and the intelligent mobile phone receives the growing status information and the disease results and displays the growing status information and the disease results. The wheat growing status diagnosing method can achieve and analyze the wheat leaf information in real time, can comprehensively reflect wheat growing situations, disease and insect situations and further improves the accuracy of wheat growing status diagnosis.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for diagnosing wheat growth status based on mobile Internet models and leaf color analysis. Background technique [0002] In the process of crop growth, plant leaves are one of the most important physiological organs of plants. The color change of leaves is closely related to the nitrogen content and chlorophyll content of crops, and the nitrogen content and chlorophyll content are also related to plant photosynthetic rate, nutrition conditions are closely related. The leaf water information can directly reflect the water status of the whole plant, thereby reflecting the water deficit of the plant, the physiological and biochemical reactions of the plant, and the growth and development status. In general, use chemical methods or special instruments to obtain crop leaf nitrogen content, chlorophyll content and other nutrient contents; use drying methods or special instrum...

AI Technical Summary

Problems solved by technology

[0005] 1. For images collected in the field, affected by the intensity of light, it may have a certain impact on the accuracy of its recognition. The lack of preprocessing to eliminate the impact of light will affect the accuracy of plant leaf information acquisition;

[0006] 2. The existing crop information acquisition method based on image, the crop information obtained is relatively single, only obtains the chlorophyll content of the crop, or only obtains the common diseases of the crop, and cannot fully reflect the growth of the crop;

[0007] 3. Most of the existing image-based crop information acquisition methods are based on the connection of computers and scanners or digital cameras to form an acquisition system, which is difficult for information acquisition and processing of field experiments

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