Third, the noisy points in the point set is further filtered out by flood filling algorithm. Second, based on exploration Sobel operator method, vertical edges detection were implemented on the gray image, eliminate the background of image and extracts binary feature point set of disease spot. At first, the captured clear disease picture of wheat leaf rust was transformed into G single channel gray image of RGB model.
The system adopted the ARM9 processor with the embedded Linux platform as the main body, and the program is developed in the Qt integrated environment. With the desire for rapid and accurate identification of disease, real-time access to the degree of that and timely measures, a wheat leaf diseases detection system based on embedded image recognition technology is designed. Wheat leaf rust is one of the major fungal diseases that makes severe drop in the wheat production. Compared with the traditional detection method, the accuracy of this detection method can reach 97%, which is close to the accuracy of the human eye, which can be used as an ambient intelligence system in the vineyard inspection, automatic completion of grape GDM detection and grading.
#Qt creator mini2440 software#
Finally, the hardware and software platform based on ARM9 integrated processor, Linux and QT is used to realize the system integration. Secondly, an ANNs is used to reduce the interference of grape leaf vein on the spot area statistics. In view of the structure of grape leaves and the distribution characteristics of GDM on leaves, firstly, image processing is mainly to solve the extraction of downy mildew areas on leaves, which is mainly composed of gray-scale processing, gray-scale linear transformation and binarization, and the effects of different processing algorithms and parameters on the results are discussed. In this paper, an accurate and intelligent Grape Downy Mildew (GDM) detection method based on common image processing and artificial neural network (ANNs) is proposed.
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