Revival of Muslin by Phuti Karpas plant identification with convolution neural network

Phuti Karpas, historically central to cotton production and thought extinct, has re-emerged in botanical research, prompting a need for reliable identification methods. This study develops a systematic approach for classifying Phuti Karpas leaves using various convolutional neural networks (CNNs), including AlexNet, Inception, VGG16, MobileNetV2, and a custom-designed baseline model. A unique dataset of 2,354 leaf images was curated, with two main classes: Phuti Karpas and Non Phuti Karpas, the latter including 14 other plant types to enhance model robustness. Each model was evaluated on metrics like accuracy, precision, recall, computational time, and memory efficiency. AlexNet yielded the highest average accuracy, while the custom baseline model, optimized for mobile deployment, provided comparable accuracy with faster inference. To demonstrate real-world usability, an Android app was created for real-time Phuti Karpas identification, offering an accessible tool for field researchers and conservationists. This work not only advances deep learning applications in plant taxonomy but also aids in the cultural and scientific revival of Phuti Karpas.