Pest pest image forecasting method based on raspberry pie

Abstract

The invention discloses a pest image forecasting method based on Raspberry Pi. At present, both manual count and electronic sensor count of the detection count of rice leaf folders of rice pests in China have respective disadvantages. A pest image forecasting system provided by the invention comprises an image and environment information collecting system, an image and environment information processing system and a wireless network transmission system. The image and environment information processing system is connected with a cloud server data processing system through the wireless network transmission system. According to the invention, the method is based on Raspberry Pi, combines an image processing technology and an electronic sensor technology, and can accurately collect a pest image and detect the number of pest individuals; through the wireless network transmission system, a worker remotely logs in the cloud server data processing system to timely and conveniently view the pest image and detection results; and the graph of the relationship among temperature, humidity, rain and the number of insects is drawn for the worker to view and export.

Description

technical field [0001] The invention belongs to the technical field of insect pest image forecasting, and in particular relates to a raspberry pie-based insect pest image forecasting method for acquiring insect pest images and counting the number of pests. Background technique [0002] As a big rice industry country in my country, the crop losses caused by the rice pest Leaf Roller are huge every year. Preventing its pest damage is a prerequisite for ensuring grain production. At present, the detection of rice leaf roller pests in my country mainly relies on two methods: one relies on sex attractants and black light to lure and capture pests, and manually retrieves various pests for sorting and counting. This method has labor efficiency Low, high intensity, poor real-time performance and other problems, and it needs to consume a lot of manpower and financial resources. Moreover, due to the heavy workload and cumbersome work content, manual errors are inevitable, making it di...

AI Technical Summary

Problems solved by technology

At present, the detection of rice leaf roller pests in my country mainly relies on two methods: one relies on sex attractants and black light to lure and capture pests, and manually retrieves various pests for sorting and counting. This method has labor efficiency Low, high intensity, poor real-time performance, etc., and it takes a lot of manpower and financial resources

Moreover, due to the heavy workload and cumbersome work content, manual errors are inevitable, making it difficult to complete accurate counting work; the second is to rely on electronic sensors to automatically count the number of pests. This method reduces the manual load to a certain extent, but lacks intuitive pests Image feedback, high false detection rate, and when the system fails, the cause of the failure is difficult to troubleshoot

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