{"title":"Identification of Nutrient Content of Psidium Guajava and Syzygium Cumini Leaves Using Hyperspectral Imaging","authors":"A. Bakiya, Venkatesh Neeli, Dhupam Mohana Lakshmi Priya, Chapala Rama Pavan","doi":"10.1109/ICSTSN57873.2023.10151662","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151662","url":null,"abstract":"The Nutrient contents of the leaf play a vital role in the plant growth, metabolism, and nutritional values of the fruits, etc. However, the conventional method for identifying the leaf nutrition content is chemical analysis, which is timeconsuming, destructive, and labor-intensive. Therefore, a nondestructive approach technique, namely hyperspectral imaging, has been increasingly used to determine the spectral characteristics of plants. This study investigated hyperspectral imaging to identify the leaf nutrition content of two leaves (Psidium Guajava and Syzygium Cumini). To identify the nutrient content of the leaf, 30 samples were taken from the two leaves with four different conditions (standard leaf, diseased leaf, dried leaf, and pigmented leaf). Further, the spectral signature of the leaves was extracted and fed into the developed regression techniques. The three different types of developed regression algorithms such as Savitzky-Golay Partial Least Squares Regression (SG-PLS), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLS), were performed for the identification of nutrition content in the leaves. The results demonstrated that the SG-PLS could accurately predict leaf nutrition content using hyperspectral imaging data, with root mean squared errors RMSE =0.536 and $mathrm{R}^{2}$=0.992 for Psidium Guajava and RMSE =1.54 and $mathrm{R}^{2}$=0.936 for Syzygium cumini leaves in normal leaf condition. Further, the results show that hyperspectral imaging can be a powerful tool for nondestructive, rapid, and accurate measurement of plant nutrient status. Also, the proposed model will identify the difference between the normal, dried, pigmented, and diseased leaves using the RMSE values.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127392091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Karthikeyan, M. Moses, P. Ramya, E. Thrisha, K. Kalarani, R. N. Susheel
{"title":"Machine Learning based Algorithmic approach for Detection and Classification of Leukemia","authors":"S. Karthikeyan, M. Moses, P. Ramya, E. Thrisha, K. Kalarani, R. N. Susheel","doi":"10.1109/ICSTSN57873.2023.10151660","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151660","url":null,"abstract":"In general, leukemia is diagnosed by taking repeated complete blood counts, since this will enormously increase the blood cell count of the patient compared to normal people. The malignant cells resemble the normal blood cell which complicates the prediction process. So, this aliment must be detected and treated in early stages to avoid any complications. The methods already existing in laboratories are time consuming. This study presents a Machine Learning approach for detecting leukemia in patients. A dataset consisting of blood smear images was collected and preprocessed to extract relevant features. The features were then used to train and test various machine learning classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and KNN. Then by evaluating the performance of the above-mentioned classifiers using different performance metrics like accuracy, precision, etc., the efficient one can be identified.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114898252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Performance Optimization of TiO2/iCh3Nh3SnI3/Cu2O Solar Cell Using Numerical Analysis","authors":"C. Tiwari, Varun Mishra, K. Deepak","doi":"10.1109/ICSTSN57873.2023.10151600","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151600","url":null,"abstract":"In this report, we have simulated intrinsic perovskite-based solar cell device (CH3 NH3 SnI3) to optimize its performance using SCPAS ID under AM 1. 5G illumination. The used ETL and HTL are TiO2 and $mathrm{c}_{mathrm{u}2}$o, respectively. The simulation is intended to focus on examining the changes in efficiency of the proposed device by variation in absorber layer thickness, defect concentration, interface states and ETL electron affinity. Furthermore, variation in the work function of back contact along with temperature was also analyzed. The obtained analysis suggests that an absorber layer thickness of l $mu$m is optimal for favorable performance of the device. Further, we analyzed that the lower absorber defect and interface defect concentration is favorable for higher efficiency. The results also suggested that work function of back contact should be greater that 5 eV for enhanced solar cell performance. The initial parameters of the materials resulted in efficiency$sim$25.6% which increased to 30.8% with optimized parameters.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}