Multi-channel capsule network ensemble for plant disease detection

• Main Author: Musa Peker
• Format: Article
• Language: English
• Published: Springer 2021-06-01
• Series: SN Applied Sciences
• Subjects: Multi-channel capsule network ensemble; Plant disease detection; Capsule networks; Ensemble learning
• Online Access: https://doi.org/10.1007/s42452-021-04694-2

Abstract This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literature