Broad-spectrum crop weed identification and positioning method based on improved deep learning

Abstract

The invention relates to the field of weed identification, in particular to a broad-spectrum crop weed identification and positioning method based on improved deep learning. The method comprises the following steps: collecting pictures of a small amount of target crops and field weeds in different growth stages; expanding a small amount of picture data into a training set and a test set on a largescale by adopting a sample enhancement technology; extracting a main area of the image; proposing a target crop feature extraction method based on a pre-training network InceptionV3, and carrying outsecondary training on the initialized model by using a new data set by adopting an improved transfer learning method on the basis of the trained model to obtain a target crop and weed recognition model; proposing an activation function for improving the network; and finely adjusting the model parameters by utilizing the test set. By adopting the method, the target crops and weeds can be quickly and accurately identified and positioned, the requirement on the acquired image data is relatively low, and the demand on the data is flexible.

Description

technical field [0001] The invention relates to the field of weed identification, in particular to an improved deep learning method for identifying and locating broad-spectrum crop weeds. Background technique [0002] Field weeds pose a great threat to the normal development of target crops, seriously affecting their high and stable yields. There are various types of weeds in the field, which grow in all seasons and require different types of herbicides for control. The traditional extensive large-scale chemical weeding has produced many negative effects, such as polluting the environment and threatening food safety. The precise variable spraying technology sprays herbicides at fixed points and quantitatively according to the distribution of weeds and crops, which can reduce the impact on the field ecological environment. In addition, it can reduce the economic cost and improve the weeding efficiency. Therefore, in combination with the current development trend of automate...

AI Technical Summary

Problems solved by technology

The computer vision technology method is to collect RGB images of weeds and crops in the field by image acquisition equipment, and analyze the morphological characteristics of weeds and crops to realize the distinction between the two. This method has requirements for image acquisition environment and image preprocessing Higher, its feature extraction process is more complicated, generally only applicable to crops and weeds of specific research objects, poor universality

The spectral technology method is to collect the respective spectral images of crops and weeds, and use the characteristics of different spectral reflection characteristics of different plants under the same lighting conditions to identify weeds. The requirements are relatively strict, and the price and learning cost of image acquisition instruments are high, and the analysis method is difficult to promote, so it is not conducive to putting into actual production

Images