Agricultural pest statistical analysis method, device, equipment and system

Abstract

The embodiment of the invention discloses an agricultural pest quantity statistical analysis method, device, equipment and system. The method comprises the steps that a pest image is received, and image boundary detection is performed on the pest image to recognize and identify positions with obvious brightness changes in the pest image; region segmentation is performed on the pest image accordingto a boundary identifier to obtain a segmentation image; a binary image is obtained according to the segmentation image, wherein the binary image comprises at least one first connection region generated according to segmentation regions; according to the pixel area of each first connection region and a preset pest pixel area, a second connection region corresponding to pests in each first connection region is acquired; and overlapping pest marking is performed on the second connection regions, and the quantity of the pests contained in the pest image is calculated according to the number of overlapping pests marked in the second connection regions. Through the agricultural pest quantity statistical analysis method, device, equipment and system, the problem that time and labor are wasted in pest recognition and quantity data acquisition is solved, and a time period with a large quantity of pests can be specifically known.

Description

technical field [0001] The invention relates to the field of electronic technology, in particular to a method, device, equipment and system for counting the number of agricultural pests. Background technique [0002] my country is a large agricultural country, and the healthy development of agriculture is related to the foundation of the country. In recent years, with the improvement of national living standards, consumers have higher and higher requirements for the quality and safety of agricultural products. In the process of agricultural production, pest control is an important factor affecting the quality of crops. Therefore, to achieve accurate identification and quantitative information of pests is the primary task of pest prediction and forecasting. If there is no correct sampling survey data, it is impossible to accurately predict the dynamics of the number of pests and the degree of pest damage, and it is impossible to ensure the correct implementation of the econo...

AI Technical Summary

Problems solved by technology

If there is no correct sampling survey data, it is impossible to accurately predict the dynamics of the number of pests and the degree of pest damage, and it is impossible to ensure the correct implementation of the economic threshold of pest control.

[0003] Traditional pest identification and counting mainly use manual identification, field investigation, trapping, etc. Manual identification and counting of farmland pests has serious problems such as low recognition rate, poor counting accuracy, and laborious field tasks due to complex and unstable field environments. Intensive, non-real-time and other shortcomings, this method can no longer meet the monitoring requirements of the serious occurrence of farmland pests; the field investigation method is time-consuming and laborious, and there are many links in data investigation, recording, and reporting, and the workload of monitoring personnel is heavy , subjective factors have great influence, and the timeliness of data application is poor, which affects the accurate prediction of pests and cannot meet the actual needs of production

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