International Research Journal on Advanced Science Hub

In this paper, our main aim is to create a Medicinal plant identification system using Deep Learning concept. This system will classify the medicinal plant species with high accuracy. Identification and classification of medicinal plants are essential for better treatment. In this system we are going to use five different Indian medicinal plant species namely Pungai, Jamun (Naval), Jatropha curcas, kuppaimeni and Basil. We utilize dataset contains 58,280 images, includes approximately 10,000 images for each species. We use leaf texture, shape, and color, physiological or morphological as the features set of the data. The data are collected by us. We use CNN architecture to train our data and develop the system with high accuracy. Several model architectures were trained, with the best performance reaching a 96.67% success rate in identifying the corresponding medicinal plant. The significantly high success rate makes the model a very useful advisory or early warning tool.

The Neural network system is an educational paradigm that unites several neural networks to solve a problem. This paper explores the relationship between the ensemble and its networks of neural components, both from the viewpoint of regression and classification, which reveals that certain networks are stronger than other neural networks. This result is surprising because the rest of the neural networks enter the ensemble at present. To prove that a GASEN algorithm efficiently selects the appropriate neural networks to construct an ensemble from different neural networks available. At first several neuronal networks were taught by GASEN. Then the network allocates random weights and uses genetic algorithms to establish these weights to classify the fitness of the neural system in one ensemble to a certain degree. Ultimately, it used the weights designed for the ensemble for certain neural networks. A comprehensive analytical analysis reveals that, in comparison to typical assemblies, such as luggage, GASEN can generate network assemblies with much smaller sizes but with a higher generalization efficiency. This study, in addition, gives the mistake a gradual regression, demonstrating that the performance of GASEN could be that it can greatly reduce its bias and uncertainty such that GASEN is well aware of its operating mechanism.