A novel ensemble learning method for crop leaf disease recognition - He, Yun; Zhang, Guangchuan; Gao, Quan (Frontiers in Plant Science, 2024)
Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models, and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf diseases recognition (named ELCDR). Unlike traditional voting strategy of ensemble learning, ELCDR assigns different weight to models based on their feature extraction performance during ensemble learning. In ELCDR, the models’ feature extraction performance is measured by the distribution of the feature vectors of training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of 4 kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape) and 1.5 (rice) percentage points in accuracy. Compared to the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape) and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall and F1-measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.