Sunflower disease recognition method based on random forest method

Abstract

The invention discloses a sunflower disease recognition method based on a random forest method. The sunflower disease recognition method can recognize four common diseases of sunflower leaves including powdery mildew, bacterial leaf spot, black spot, and downy mildew and comprises steps of: A: disease image acquisition in which an acquired leaf image color is required to be as close as possible to the color of a leaf itself; B: disease image processing in which a processing method suitable for sunflower disease image recognition is used; C: disease image segmentation in which an optimal color image segmentation method is selected by analyzing and comparing various image segmentation methods; D: disease image feature extraction in which parameters such as the color feature and the texture feature of the disease image are extracted for research; and E: disease recognition and diagnosis in which the sunflower disease is diagnosed and recognized by using the random forest method. The sunflower disease recognition method mainly solves the subjectivity, the limitation, and the fuzziness of eye determination and difficulty in new disease determination in a process of disease recognition, improves the accuracy of diseases recognition, and provides agricultural workers with good help for recognizing and preventing sunflower diseases.

Description

technical field

[0001] The invention relates to the field of identification of agricultural diseases and insect pests, in particular to a sunflower disease identification method based on a random forest method. Background technique

[0002] As we all know, the traditional method of disease diagnosis is mainly for plant protection personnel to identify with naked eyes, combined with the form of pathogenic bacteria of plant diseases to judge. This method has low diagnostic efficiency, and it is difficult to judge the type of disease in a timely and accurate manner. "Precision agriculture" provides farmers with new ideas. By using information technology to quickly and effectively identify plant diseases, compared with traditional identification methods, the identification speed is fast, the accuracy is high, and it is time-sensitive. Taking crop diseases such as apples, cucumbers and peppers as examples, the images of plant leaf diseases were analyzed, and the color information...

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