Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification

  • Joao Paulo Schwarz Schuler DEIM, Universitat Rovira i Virgili, Spain
  • Santiago Romani DEIM, Universitat Rovira i Virgili, Spain
  • Mohamed Abdel-Nasser DEIM, Universitat Rovira i Virgili, Spain; Electrical Engineering Department; Aswan University, Aswan, Egypt
  • Hatem Rashwan DEIM, Universitat Rovira i Virgili, Spain
  • Domenec Puig DEIM, Universitat Rovira i Virgili, Spain
Keywords: CNN, DCNN, Deep Learning, Plant Disease, CIE LAB, Neural Networks, Artificial Intelligence, Multipath


Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.


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How to Cite
Schwarz Schuler, J.P., Romani, S., Abdel-Nasser, M., Rashwan, H. and Puig, D. 2022. Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification. MENDEL. 28, 1 (Jun. 2022), 55-62. DOI: