Enhancing Medicinal Plant Classification and Supply Chain Integrity Using CNN and LSTM Networks
Keywords:
Classification, CNN-LSTM, Deep learning, Medicinal plant dataset, Medicinal plants classificationAbstract
Medicinal plants Classification is important since there might look like and this makes their identification significant in paying health care, pharmaceutical industries, and conservations of the herbal plants. However, the classification methods exist but they deal with the high-dimensional image data, especially for the plants that share some similarity in morphology. To overcome the challenges, the current rese arch work uses Convolutional Neural Network (CNN) in order to extract features very efficiently and Long-Term Short Memory (LSTM) network for sequential data analysis that significantly improve the recognition ability of plant images. Medicinal Plant Dataset, having 30 classes of varied medicinal plants each having around 500 images, was utilized for training and testing the work. The efficiency of our hybrid model is depicted in the Python code platform as it processes diverse complex visual patterns. The proposed system evaluation attained 99.2% classification accuracy, indicating the reliability of this proposed system compared to traditional methods and the computational time is low. Medicinal plant identification using CNN-LSTM helps botanists, researchers, and industries classify efficiently, conserve, and sustainably utilize medicinal plants. This innovation provides better accuracy for the classification of plants. The proposed system will prove to be most useful for botanists, researchers in herbal medicine, and the pharmaceutical industries as it is going to enable them to have an efficient means of identification of medicinal plants. It can therefore help in protecting biological diversity and the sustainable utilization of medicinal plants, thereby guiding in new solutions of physic to nature's cure.
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