Method and device for monitoring germination rate of acupoint planting based on UAV remote sensing

Abstract

The present invention provides a method and device for monitoring germination rate of hole sowing based on UAV remote sensing. Monitor the contour shape and size characteristics of the unit, and divide the overlapping plant unit and the single plant unit; use the data statistics method to determine the number of pixels covered by a single plant, and based on the number of pixels covered by the single plant Calculate the overlapping times of plants in the overlapping plant units; by counting the total number of the single plant units and the overlapping times of each overlapping plant unit, calculate the total number of budding plants, and based on the total number of budding plants Number and total sowing point number, calculate the germination rate of the target point point sowing. The invention can effectively improve the monitoring efficiency, accuracy and applicability of the germination rate of crops, thereby greatly saving the consumption of human resources and improving the information management level.

Description

technical field [0001] The invention relates to the field of agricultural information technology, and more specifically, to a method and device for monitoring germination rate of acupoint planting based on remote sensing of drones. Background technique [0002] Rapid and accurate monitoring of crop germination after sowing is of great significance for timely replanting management of plots with low germination rate, thereby effectively improving land productivity, saving costs, and improving production efficiency. [0003] At present, the monitoring of the germination rate of crops is generally realized through manual visual search and statistics in the farmland. Or, by collecting images of crop planting areas, dividing the images into pixel groups with a fixed number of pixels, and then classifying each pixel group to obtain green crop vegetation and surface soil. Then perform topological conversion on the obtained pixels classified as crop vegetation to obtain the vector o...

AI Technical Summary

Problems solved by technology

[0004] The above-mentioned artificial plot survey method has high precision and good effect, but it is very labor-intensive and inefficient, especially in large-scale crop planting areas, often because some crops in the farmland cannot be replanted in time after they germinate , which affects subsequent crop yields

And, with rising labor costs, this method will become increasingly difficult to be widely used

[0005] However, by collecting images of crop plots, and performing pixel group division, classification, topology conversion, and counting methods, since a fixed pixel number standard is used for pixel group division, and the image data is processed in units of pixel groups treatment, while ignoring plant overlap issues and plant size issues, resulting in a bias in the estimated germination rate

For example, due to the difference in the field environment, the crop plants that germinate in adjacent sowing points may overlap, which will cause the estimated germination rate to be lower than the actual situation, or be divided into multiple crops due to a large plant. tuples, resulting in double counting and thus high estimates

Images