Herbal Medicinal Plant Identification Using Leaf Vein Through Image Processing and Convolutional Neural Network

The utilization of herbal medicinal plants dates to antiquity, and as human civilization has progressed and technology has advanced, a significant proportion of contemporary medicines have originated from herbal sources. The Philippines is renowned for its extensive utilization of herbal medicinal plants, exemplified by the Department of Health's recognition of ten prominent herbal medicines under the “Traditional and Alternative Medicine Act (TAMA)”, or the “Republic Act No. 8423”. This legislative measure not only enhances the healthcare system within the country but also underscores the nation's commitment to incorporating traditional healing methods. Many herbal medicinal plants possess valuable therapeutic properties; however, the lack of comprehensive research and clinical trials has resulted in limited knowledge regarding the specific benefits associated with each of these plants. This research endeavor culminated in developing a device capable of identifying the herbal medicinal plant name, scientific name, and medicinal purposes by capturing a leaf image. This study's initial phase entails pre-processing the captured image and extracting the leaf vein characteristics using the Histogram of Oriented Gradient (HOG) feature extraction algorithm. Subsequently, the Convolutional Neural Network (CNN) algorithm is employed to identify the leaf based on these extracted features. Following the execution of 25 trials for each of the five selected herbal medicinal plants, namely Banaba, Bayabas, Bignay, Sambong, and Serpentina, in addition to 25 tests for two untrained plants, the research findings demonstrated a commendable accuracy rate of 95%.