WECNN-PDP: Weighted Ensemble Convolutional Neural Networks Models to Improve the Plant Disease Prediction

Abstract

As an agricultural country, Indonesia’s agricultural production is essential. However, crop failure will occur if diseases and other factors, such as natural disasters, attack many plant fields. These problems can be minimized by early detection of plant diseases. However, detection will be challenging if done conventionally. Prior research has shown that deep learning algorithms can perform detection with promising results. In this study, we propose a new weighted deep learning ensemble method as a solution for better performance in plant disease detection. We ensemble the model by considering the combination of two and three pre-trained convolutional neural networks (CNNs). Initially, we perform transfer learning on individual CNN models by prioritizing high-dimensional features through weight updates on the last few layers. Finally, we ensemble the models by finding the best weights for each model using grid search. Experimental results on the Plant Village dataset indicate that our model has improved the classification of 38 plant diseases. Based on metrics, the three-model ensemble performed better than the two-model ensemble. The best accuracy results of the ensemble MobileNetV2-DenseNet121 and MobileNetV2-Xception-DenseNet121 models are 99.49% and 99.56%, respectively. In addition, these models are also better than the state-of-the-art models and previous feature fusion techniques we proposed in LEMOXINET. Based on these results, the ensemble technique improved the detection performance, and it is expected to be applied to real-world conditions and can be a reference to be developed further in future research.