Grape Disease Evaluation Using a Mixed-Model Ensemble of Convolution Neural Network and Random Forest

Abstract Identifying and detecting crop diseases before they spread is a critical task for any farmer. Deep learning and computer vision are both useful for learning image features used in image classification tasks; however, deep learning takes a long time to train. In this paper, we propose an ensemble model for detecting pests on grape leaf images that employs a convolution neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. CNN-based models VGG16, InceptionV3, Xception, and ResNet50 are arranged in parallel of two- and three-pathway configurations to extract more features and given to the RF for classification, and their accuracy is compared to a one-way model. Using the same data, we found that arranging CNN-based models in parallel configurations improves accuracy by 3.43% for two-pathway models and 6.37% for three-pathway models. Moreover, the best model in the one-way setting achieved 89.22% accuracy, while the best models in the two- and three-pathways settings achieved 92.65% and 95.59% accuracy, respectively.