Abstract:

Accurate and rapid identification of alfalfa diseases is the key to disease prevention and control in alfalfa grassland. The identification of alfalfa diseases requires a high degree of professional knowledge， identification tools， and a suitable detection environment.Traditional methods for identifying alfalfa diseases include microscopic observations and other means to inspect the diseased parts of the leaves to detect pathogen strains. This disadvantages of those methods are their poor timeliness， high cost， and inability to identify diseases rapidly at multiple locations on a large scale. In recent years， computer-aided methods and deep learning in the field of image recognition have developed rapidly， providing new methods for the intelligent identification of alfalfa diseases. In this study， an alfalfa disease identification model was constructed using image datasets of 13 common alfalfa diseases， the improved AlexNet deep learning convolutional neural network， and 300 iterations of training. The recognition accuracy of alfalfa diseases under different image input resolutions was compared and analyzed. The optimal model for identifying 13 types of alfalfa diseases achieved an overall accuracy of 72%， and the optimal size of the image input was 512 pixels×512 pixels. After removing the images of diseased alfalfa samples with low recognition accuracy， the overall recognition accuracy of five alfalfa diseases， namely brown spot disease， downy mildew disease， anthracnose， black stem and leaf spot disease， and little light lenticel spot disease was increased to 92%， and the optimal input image size was 1200 pixels×1200 pixels. These two models are suitable for the rapid identification of major alfalfa diseases. These results provide technical support for the development of intelligent detection systems for alfalfa diseases based on image recognition.

Key words: alfalfa disease, AlexNet, target detection, deep learning