http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/41 2026-04-18T13:47:12Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/432 An Efcient Mobile Application for Identifcation of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm Thanikkal, Jibi G.; Dubey, Ashwani Kumar; Thomas, M. T. In the Covid-19 pandemic situation, the world is looking for immunity-boosting techniques for fghting against coronavirus. Every plant is medicine in one or another way, but Ayurveda explains the uses of plant-based medicines and immunity boosters for specifc requirements of the human body. To help Ayurveda, botanists are trying to identify more species of medicinal immunity-boosting plants by evaluating the characteristics of the leaf. For a normal person, detecting immunity-boosting plants is a difcult task. Deep learning networks provide highly accurate results in image processing. In the medicinal plant analysis, many leaves are like each other. So, the direct analysis of leaf images using the deep learning network causes many issues for medicinal plant identifcation. Hence, keeping the requirement of a method at large to help all human beings, the proposed leaf shape descriptor with the deep learning-based mobile application is developed for the identifcation of immunity-boosting medicinal plants using a smartphone. SDAMPI algorithm explained numerical descriptor generation for closed shapes. This mobile application achieved 96%accuracy for the 64×64 sized images. 2023-04-21T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/395 Deep Learning based Aquatic and Semi Aquatic Plants Morphological Features Extraction and Classification Thanikkal, Jibi G; Dubey, Ashwani Kumar; Thomas, M. T. In Ayurveda, the ancient medicinal plant identification system is based on the morphological comparison of leaf, fruit, flower, root, stem etc. Botanists use morphometrics for aquatic and semi-aquatic medicinal plants classification. However, deep learning networks provide the highest image classification result in digital image processing. Existing deep learning algorithms generate feature maps for pixel-wise image classification. In the feature map of deep learning output, most of the morphological features are missing. This issue leads to the Catastrophic forgetting issue of deep learning. To generate a traditional morphological feature-based medicinal plant identification system, we are introducing morphometrics and morphological feature-based deep learning networks for aquatic and semi-aquatic plant classification. This article contains: (a) A detailed morphological features database of aquatic and semi-aquatic medicinal plants, (b) a summary of the importance of the morphological features-based leaf classification, (c) a morphological features extraction algorithm and (d) the morphological features-based deep learning approach for aquatic and semi-aquatic plant classification. This human brain-like procedure achieved 97% classification accuracy and reduced the Catastrophic forgetting issue of continual learning. 2022-10-01T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/175 Whether color, shape and texture of leaves are the key features for image processing based plant recognition? An analysis! Thanikkal, Jibi G; Dubey, Ashwani Kumar; Thomas, MT Studies on plant identification through image processing consider shape, color and texture features of leafs. But botanist's uses leaf morphology, leaf arrangement, types of venation, leave shapes, leave bases, leaf margins and leaf apices for recognizing a plant. This paper introduces the leaf venation, leaf margin, leaf apies, and leaf bases models for improving plant leaf identification. These new features along with shape, color and feature increases the accuracy of the plant identification. 2017-01-01T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/172 Advanced Plant Leaf Classification Through Image Enhancement and Canny Edge Detection Thanikkal, Jibi G; Dubey, Ashwani Kumar; Thomas, MT Accuracy in identification of any plant is achieved by understanding and extracting the plant features. Image processing techniques has gained interest in identifying the plants in realist and accurate manner. Among them, Edge detection techniques has very important role in creation of database for plant identification. Edge filtering and optimization technique to create continuous edges makes Canny edge detection widely popular to retrieve image characteristics. The proposed contour based image segmentation process filter morphological features from plant leaves. Detailed vein and texture extraction of plant leave using Canny edge detector are explained. 2018-01-01T00:00:00Z