Kestrilia Rega Prilianti, Tatas Hardo Panintingjati Brotosudarmo, Syaiful Anam, Agus Suryanto
In this study, we developed a new fuzzy logic-based convolution layer on a two-dimensional Convolutional Neural Network (2D-CNN). This innovation aims to enhance the ability of CNN in recognizing colors. We experimented on P3Net, which is a 2D-CNN model that is used to predict photosynthetic pigment content in plant leaves in real time and non-destructive manner. The P3Net is designed to be able to predict three main photosynthetic pigment content (chlorophyll, carotenoid, and anthocyanin) based on the leaves color. The leaf colors were captured in the form of an RGB image and the image was used as the CNN input. We compare the performance of P3Net with and without the fuzzy logic-based convolution layer. It was revealed that the new form of convolution layer could significantly improve the P3Net performance. © 2024 American Institute of Physics Inc.. All rights reserved.
Department of Informatics Engineering, Universitas Ma Chung, Villa Puncak Tidar N-01, Malang, 65151, Indonesia; Department of Food Technology, Universitas Ciputra, Citra Land CBD Boulevard, Surabaya, 60219, Indonesia; Department of Mathematics, Universitas Brawijaya, Jl. Veteran, Malang, 65145, Indonesia
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