关键词: 甜瓜, 白粉病, 高光谱, 特征波长, 机器学习, 早期识别

Abstract: Powdery mildew is one of the major diseases affecting the yield and quality of melon, hyperspectral technology was used to realize the early disease identification of melon powdery mildew. By using greenhouse melon as the research object, hyperspectral images of melon leaves containing 128 bands were collected, in which leaves within 1-4 days of inoculation with powdery mildew fungus were classified as the early diseased leaves and leaves with no fungus inoculation were healthy ones. Two algorithms, such as Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS), were used to extract characteristic wavelengths, and Principal Component Analysis (PCA) was applied to reduce the dimensionality of the original data. The original wavelength (Original), SPA characteristic wavelength (8), CARS characteristic wavelength (9) and PCA principal components (4) were used as the input variables of the recognition models, respectively. Combined with two ensemble leaning methods, Random Forests (RF) and Adaptive Boosting (AdaBoost). Eight early identification models of melon powdery mildew were constructed, including Original-RF, SPA-RF, CARS-RF, PCA-RF, Original-AdaBoost, SPA-AdaBoost, CARS-AdaBoost, PCA-AdaBoost. The model was evaluated by the ten-fold cross-validation method. The results showed that the accuracy of the proposed models were all above 90%, among which the Original-AdaBoost and Original-RF models had the highest average accuracy of 94.3% and 93.8%, respectively. SPA-AdaBoost effectively reduced the model input and achieved 93.3% recognition accuracy on the 1st day of the disease, with an average accuracy of 93.5%. 

Key words: melon, powdery mildew, hyperspectral, characteristic wavelength, machine learning, early identification