Automatic Classification of Medicinal Plants Using State-Of-The-Art Pre-Trained Neural Networks

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity.  It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges.  Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%

Evaluation System for Craniosynostosis Surgeries with Computer Simulation and Statistical Modelling

Craniosynostosis is a pathology in infants when one or more sutures prematurely closed, leading to abnormal skull shape. It has been classified according to the specific suture that has been closed, each of which has a typical skull shape. Surgery is the common treatment to correct the deformed skull shape and to reduce the excessive intracranial pressure. Since every case is unique, the cranial facial teams have difficulties to select an optimum solution for a specific patient from multiple options. In addition, there is not an appropriate quantified measurement existed currently to help cranial facial team to quantitatively evaluate their surgeries. We aimed to develop a head model of a craniosynostosis patient, which allows neurosurgeons to perform any potential surgeries on it so as to simulate the postoperative head development. Therefore, neurosurgeons could foresee the surgical results and is able to select the optimal one. In this thesis, we have developed a normal head model, and built mathematical models for possible dynamic behaviors. We also modified this model by closing one or two sutures to simulate common types of craniosynostosis. The abnormal simulation results showed a qualitative match with real cases and the normal simulation indicated a higher growth rate of cranial index than clinical data. We believed that this discrepancy caused by the rigidity of our skull plates, which will be adapted to deformable object in the future. In order to help neurosurgeons to better evaluate a surgery, we hope to develop an algorithm to quantify the level of deformity of a skull. We have designed a set of work flow and targeted curvatures as the key role. A training data was carefully selected to search for an optimal system to characterize different shapes. A set of test data was used to validate our algorithm to assess the performance of the optimal system. With a stable evaluating system, we can evaluate a surgery by comparing the preoperative and postoperative skulls from the patient. An effective surgery can be considered if the postoperative skull shifted toward normal shape from preoperative shape

Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing