PB3C-CNN: An integrated PB3C and CNN based approach for plant leaf classification

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Plant identification and classification are critical to understand, protect, and conserve biodiversity. Traditional plant classification requires years of intensive training and experience, making it difficult for others to classify plants. Plant leaf classification is a challenging issue as similar features appears in different species of plant. With the development of automated image-based classification, machine learning (ML) is becoming very popular. Deep learning (DL) methods have significantly improved plant image identification and classification. In the last decade, convolutional neural networks (CNN) have entirely dominated the field of computer vision, showing outstanding feature extraction capabilities and significant identification and classification performance. The capability of CNN lies in its network. The primary strategy to continue this trend in the literature relies on further scaling networks in size. However, costs increase rapidly, while performance improvements may be marginal when the number of net-works increases. Hence, there is a need to optimize the CNN network to get the best possible result with the minimum number of networks and other parameters such as the number of epochs, number of layers, batch size and number of neurons. The paper aims to evolve the optimal architecture of CNN using PB3C algorithm for plant leaf classification. For this, we use the nature-inspired computing technique parallel big bang–big crunch to evolve a CNN's optimal architecture automatically. Current study validated the proposed approach for plant leaf classification and compared it with 11 other machine learning-based approaches. From the results obtained it was found that the proposed approach was able to outperforms all 11 existing state-of-the-art techniques.