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Kavitha, A.
- The Floristic Composition of Sacred Groves - a Functional Tool to Analyse the Miniforest Ecosystem
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Indian Forester, Vol 131, No 6 (2005), Pagination: 773-785Abstract
Sacred groves are one of the traditional, informal conservation concepts for preserving bio-diversity. Present study has brought to light 329 plant species from 251 genera belonging to 110 families from 40 sacred groves. Out of the 110 families, 108 belonging to angiosperms and two to gymnosperms. In the floristic composition also, they are very rich with 139 species of trees, 95 shrubs, 79 herbs and 16 Iianas, climbers and twiners. Nearly 88% plants are of dicots, monocots 11.25% and gymnosperms just represent only 0.61 % from the overall record of flora. Of the 329 species enumerated, 54 are listed rare, endemic and threatened. The groves from the Vilavancode Taluk floristic diversity is the richest (37.9%), followed by Kalkulam, Thovalai and Agastheeswaram taluks. The dominant family was Fabaceae with 16 species to its credit recorded from these groves. The phytogeographic analysis of flora showed that the Asiatic element is predominant, followed by Indian and the Endemics. In the critical observation two Keystone and four Flagship species were identified. Creating awareness among local people, educating all those who are associated in the management of the sacred groves and nearby residents are important in their conservation.
- Company Trend Analysis Using Subspace Clustering and Frequent Patterns
Abstract Views :130 |
PDF Views:4
Authors
Affiliations
1 Park‟s College, Chinnakari, IN
2 Dept of computer Science, Park‟s College, Chinnakari, IN
1 Park‟s College, Chinnakari, IN
2 Dept of computer Science, Park‟s College, Chinnakari, IN
Source
Software Engineering, Vol 6, No 8 (2014), Pagination: 223-225Abstract
Clustering techniques and frequent pattern mining methods are used to discover events in company data analysis. Feature selection method is used for identifying a subset of the most needed features, it produces compatible results. A feature selection algorithm is constructed with the consideration of efficiency and effectiveness factors.
Data models are analyzed with different dimensions. Object, attribute and context information are linked in the 3 dimensional data models. Cluster quality is decided with domain knowledge and parameter setting requirements.CAT Seeker is also referred as a Centroid Actionable 3D subspace clustering framework. CAT Seeker framework is used to find profitable actions. Singular value decomposition, numerical optimization and 3D frequent itemset mining methods are integrated in CAT Seeker model. Singular value decomposition (SVD) is used to calculating and pruning the homogeneous tensor. Augmented Lagrangian Multiplier Method is used to calculating the probabilities of the values. 3D closed pattern mining is used to fetch Centroid-Based Actionable 3D Subspaces (CATS).
Clustring and pattern mining techniques are integrated in the CATSeeker method. CAT Seeker framework is improved with optimal centroid estimation scheme. Intra cluster accuracy factor is used to fetch centroid values. Inter cluster distance is also considered in centroid estimation process. Dimensionality analysis is applied to improve the subspace selection process.
Keywords
Clustering, Centroid Based 3D-Subspace Clustering, Singular Vector Decomposition.- Cancer Detection Using Hyperspectral Imaging and M-FISH Technique
Abstract Views :148 |
PDF Views:3
Authors
Affiliations
1 Bharathiar University, Coimbatore, IN
2 Vinayaga Missions University, IN
1 Bharathiar University, Coimbatore, IN
2 Vinayaga Missions University, IN
Source
Digital Image Processing, Vol 3, No 12 (2011), Pagination: 750-754Abstract
Medical imaging plays a crucial role in the diagnosis of various diseases. Modern technology has made many innovations for diagnosing the disease so that human race can lead a healthy life. One of such innovation is medical imaging. Along with MRI (Magnetic Resonance Imaging), Computed Tomography (CT), Ultrasonopgraphy a new technology called Hyperspetral imaging is gaining popularity in medical field. This paper provides an overview about the Hyperspectral technology and the algorithms used. We have discussed the hyperspectral image along M-FISH technique act as an efficient way to detect the cancer in its early stage. The main challenge in this method is the overlapping of the spectrum of the fluorescent colors in the hyperspectral image. We have presented a review of spectral unmixing algorithms which are used in classification of overlapping of spectrum of fluorescent colors and suggested a combination of algorithms to effectively reduce the overlapping of spectral.Keywords
Autofluoroscence, Flourochromes, Hyperspectral, M-FISH, VARIMAX.- Texture Based Image Classification and Retrieval Using Fuzzy Clustering and HMM Classifier Approach
Abstract Views :145 |
PDF Views:3
Authors
P. Janarthanan
1,
A. Kavitha
2
Affiliations
1 Sri Venkateswara College of Engineering Sriperumbudur, Chennai, IN
2 RMK College of Engineering, Kaverapettai, Gummudipoondi, IN
1 Sri Venkateswara College of Engineering Sriperumbudur, Chennai, IN
2 RMK College of Engineering, Kaverapettai, Gummudipoondi, IN
Source
Digital Image Processing, Vol 1, No 4 (2009), Pagination: 145-148Abstract
Image classification is major area of research. Many of the researchers doing research in this area to find optimal methods or algorithms to classify the query image into relevant image or mimages. The proposed work focuses on textures based image classification and retrieval. Initially, the image features are extracted from images and reduced dimensions of image features using principal component analysis (PCA) method. Then the segmentation process involves grouping of the similar texture components into several groups. We used Fuzzy C-Mean clustering (FCM) to segment the image. So, we get the required sub-image feature for classification. During the classification, Hidden markov model (HMM) is used to match the unknown texture against different set of mimage classes. Finally the best match is taken as the classification result.
Keywords
PCA, Fuzzy C-Mean (FCM), K-Mean, HMM.- A Comparative Study on Fusion Strategies in Multimodal Biometric System
Abstract Views :321 |
PDF Views:5
Authors
A. Kavitha
1,
A. Vanaja
2
Affiliations
1 PSGR Krishnammal College for Women, Bharathiyar University, Coimbatore, IN
2 Add.st.Aloysius College (Autonomous), Mangalore University, Mangalore-575005, Karnataka, IN
1 PSGR Krishnammal College for Women, Bharathiyar University, Coimbatore, IN
2 Add.st.Aloysius College (Autonomous), Mangalore University, Mangalore-575005, Karnataka, IN
Source
Biometrics and Bioinformatics, Vol 5, No 12 (2013), Pagination: 413-416Abstract
Most of the business applications use biometrics to authenticate and verify the person when a transaction is made. Biometric systems are of two types: unimodal and multimodal. unimodal biometrics use only single trait like fingerprint, iris, face and retina (physiological trait) or gait, voice, handwritten (behavioral trait) to verify the person. But it suffers from some limitations of noise in sensed data, intra-class variation, inter-class similarities, non-universality and spoof attacks. Multimodal biometric systems overcome some of these limitations through fusion process. Multimodal biometric system provides more accuracy when compared to unimodal biometric system. The main goal of multimodal biometric system is to develop the security system for the areas that require high level of security. a reliable and successful multimodal biometric system needs an effective fusion scheme to combine biometric characteristics derived from one or modalities. The goal of fusion is to determine the best set of experts in a given problem domain and helps to minimize the error rate. It also improves accuracy, efficiency, and system robustness and fault tolerance. In this survey different fusion techniques of multimodal biometrics have been discussed.
Keywords
Biometrics, Unimodal, Multimodal Biometrics, Fusion.- Studies on the Development and Evaluation of Colon Targeted Mesalamine Tablets Based on Eudragit L100-Chitosan Interpolyelectrolye Complexes
Abstract Views :236 |
PDF Views:1
Authors
Affiliations
1 Department of Pharmaceutics, Malla Reddy College of Pharmacy (MRCP), Maisammaguda, Dhulapally, (Post Via Hakimpet), Secunderabad, A.P-500014, IN
1 Department of Pharmaceutics, Malla Reddy College of Pharmacy (MRCP), Maisammaguda, Dhulapally, (Post Via Hakimpet), Secunderabad, A.P-500014, IN
Source
Research Journal of Pharmacy and Technology, Vol 10, No 5 (2017), Pagination: 1289-1296Abstract
An attempt was made to prepare and evaluate colon targeted Mesalamine tablets based on interpolyelectrolyte complex. Tablets were prepared by wet granulation technique using varying ratios of Eudragit L100 (EL) and Chitosan (CS) polymers. Results obtained showed that the tablets conformed to compendial requirements for acceptance and CS and EL formed IPECs that showed pH-dependent swelling properties and prolonged the in vitro release of Mesalamine from the tablets in the following descending order: 3 : 2 > 2 : 3 > 1 : 1 ratios of CS and EL. An electrostatic interaction between the carbonyl (-CO-) group of EL and amino (-NH3 +) group of CS of the tablets formulated with the IPECs was capable of preventing drug release in the stomach and small intestine and helped in delivering the drug to the colon. Kinetic analysis of drug release profiles showed that the systems predominantly released Mesalamine in a zero-order manner.Keywords
Mesalamine, Colon Targeting, Electrostatic Interaction, IPEC, in Vitro Release.- Missing Value Imputation and Normalization Techniques in Myocardial Infarction
Abstract Views :249 |
PDF Views:2
Authors
K. Manimekalai
1,
A. Kavitha
2
Affiliations
1 Department of Computer Applications, Sri GVG Visalakshi College for Women, IN
2 Department of Computer Science, Kongunadu Arts and Science College, IN
1 Department of Computer Applications, Sri GVG Visalakshi College for Women, IN
2 Department of Computer Science, Kongunadu Arts and Science College, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 3 (2018), Pagination: 1655-1662Abstract
Missing Data imputation is an important research topic in data mining. In general, real data contains missing values. The presence of the missing value in the data set has a major problem for precise prediction. The objective of this paper is to highlight possible improvement of existing algorithm for medical data. KNBP imputation method based on KNN and BPCA is proposed and evaluate MSE and RMSE estimates. Normalization is done by comparing three algorithms namely min-max normalization, Z-score and decimal scaling. The experiment is done with standard bench mark data and real time collected data. KNBP imputation method and Decimal Scaling Algorithm for Normalization got lower error rate.Keywords
Mean, Hot Deck, KNN, BPCA, KNBP, Min-Max Algorithm, Z-Score, Decimal Scaling.References
- A. Sudha, P. Gayathri and N. Jaishankar, “Utilization of Data Mining Approaches for Prediction of Life Threatening Disease Survivability”, International Journal of Computer Applications, Vol. 14, No. 17, pp. 51-56, 2012.
- M. Durairaj and S. Sivagowry, “A Pragmatic Approach of Preprocessing the Data Set for Heart Disease Prediction”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 11, pp. 23-29, 2014.
- Tahani Aljuaid and Sreela Sasi, “Proper Imputation Techniques for Missing Values in Data sets”, Proceedings of International Conference on Data Science and Engineering, pp. 168-176, 2016.
- Peter Schmitt, Jonas Mandel and Mickael Guedj, “A Comparison of Six Methods for Missing Data Imputation”, Journal of Biometrics and Biostatistics, Vol. 6, No. 1, pp. 1-6, 2015.
- Vinod Bharat, Balaji Shelale, K. Khandelwal and Sushant Navsare, “A Review Paper on Data Mining Techniques”, International Journal of Engineering Science and Computing, Vol. 4, No. 5, pp. 1976-1979, 2016.
- Suad A. Alasadi and Wesam S. Bhaya, “Review of Data Preprocessing Techniques in Data Mining”, Journal of Engineering and Applied Sciences, Vol. 12, No. 16, pp. 4102-4107, 2017.
- J.F. Mac Gregor and T. Kourti, “Statistical Process Control of Multivariate Processes”, Control Engineering Practice, Vol. 3, No. 3, pp. 403-414, 1995.
- R. Dunia, S.J. Qin and T.F. Edgar, “Identification of Faulty Sensors using Principal Component Analysis”, AICHE Journal, Vol. 42, No. 10, pp. 2797-2812, 1996.
- R. Little, “Statistical Analysis with Missing Data”, 2nd Edition, Wiley Press, 2002.
- Nazri Mohd Nawi, Walid Hasen Atomi and M. Z. Rehman, “The Effect of Data Pre-Processing on Optimized Training of Artificial Neural Networks”, Procedia Technology, Vol. 11, pp. 32-39, 2013.
- Bhavisha Suthar, Hemant Patel and Ankur Goswami, “A Survey: Classification of Imputation Methods in Data Mining”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 1, pp. 309-312, 2012.
- Runmin Wei, Jingye Wang, Mingming Su, Erik Jia, Tianlu Chen and Yan Ni, “Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data”, Scientific Reports, Vol. 8, pp. 663-674, 2017.
- Kristen A. Seversaon, Mark C. Molaro and Richard D. Braatz, “Principal component Analysis of process Datasets with Missing Values”, Processes, Vol. 5, No. 3, pp. 38-49, 2017.
- Hiroshi De Silva and A. Shehan Perera, “Missing Data Imputation using Evolutionary K-Nearest Neighbor Algorithm for Gene Expression Data”, Proceedings of 6th International Conference on Advances in ICT for Emerging Regions, 2017.
- Dan Zeng, Dan Xie, Ran Liu and Xiaodong Li, “Missing Value Imputation Methods for TCM Medical Data and its Effect in the Classifier Accuracy”, Proceedings of 16th International Conference on Advances in ICT for Emerging Regions, pp. 339-354, 2017.
- S. Thirukumaran and A. Sumathi, “Improving Accuracy Rate of Imputation of Missing Data using Classifier Methods”, Proceedings of 10th International Conference on Intelligent Systems and Control, pp. 1243-1251, 2017.
- Shichao Zhang, Debo Cheng, Zhenyun Deng, Ming Zong and Xuelian Deng, “A Novel KNN Algorithm with Data Driven K Parameter Computation”, Pattern Recognition Letters, 2017.
- Xianglin Yang, Yunhai Tong, Xiang Shuai Zhao, Zhi xu, Yanjunli, Xin Jia and Shaohna Tan, “Adaptive Logistic Group Lasso Method for Predicting the No-Reflow among the Multiple Types of High Dimensional Variables with Missing Data”, Proceedings of 7th International Conference on Software Engineering and Service Science, pp. 461-469, 2017.
- Mehran Amiri and Richard Jensen, “Missing Data Imputation using Fuzzy-Rough Methods”, Neurocomputing, Vol. 205, pp. 152-164, 2016.
- Asma Saleem, Khadim Hussain Asif, Ahmad Ali and Shahid Mahmood Awan, “Pre-Processing Methods of Data Mining” Proceedings of 7th International Conference on Utility and Cloud Computing, pp. 651-663, 2014.
- Xiangyang Liu, “A Preprocessing Method of AdaBoost for Mislabeled Data Classification”, Proceedings of 29th International Conference on Control and Decision, pp. 23-32, 2017.
- A. Daraei, H. Hamaidi, “An Efficient Predictive Model for Myocardial Infarction using Cost-sensitive J48 Model”, Iran Journal of Public Health, Vol. 46, No. 5, pp. 682-692, 2017.
- Thripurna Thatipelli and Padmavathi Kora, “Classification of Myocardial Infarction using Discrete Wavelet Transform and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 429-432, 2017.
- V. Hemalatha and C. Usha Nandhini, “An Efficient Approach for Constructing a Model for Diagnosing Heart Disease Dataset”, International Journal of Contemporary Research in Computer Science and Technology, Vol. 3, No. 3, pp. 41-44, 2017.
- Sarab AlMuhaideb, “An Individualized Preprocessing for Medical Data Classification”, Procedia Computer Science, Vol. 82, pp. 35-42, 2016.
- Hojat Hamidi and Atefeh Daraci, “A New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction”, Journal of Community Health Research, Vol. 5, No. 2, pp. 110-120, 2016.
- Muhammad Sheikh Sadi, et al., “A New Approach to Extract Features from ECG Signals”, Proceedings of 2nd International Conference on Electrical Information and Communication Technology, pp. 189-194, 2015.
- S. Selva Nithyananthan, S. Saranya and R. Santha Selva Kumari, “Myocardial Infarction Detection and Heart Patient Identity Verification”, Proceedings of International Conference on Wireless Communications, Signal Processing and Networking, pp. 1107-1111, 2016.
- Manuel Martin Salvador, Marcin Budka and Bogdan Gabrys, “Effects of Change Propagation Resulting from Adaptive Preprocessing in Multicomponent Predictive Systems”, Procedia Computer Science, Vol. 96, pp. 713-722, 2016.
- V. Seenivasagam and R. Chitra, “Myocardial Infarction Detection using Intelligent Algorithms”, Neural Network World, Vol. 1, pp. 91-110, 2016.
- S. Gopal Krishna Patro, Kishore Kumar sahu, “Normalization: A Preprocessing Stage”, Available at: https://arxiv.org/ftp/arxiv/papers/1503/1503.06462.pdf.
- Padmavathi Kora and Sri Ramakrishna Kalva, “Improved Bat Algorithm for the Detection of Myocardial Infarction”, Springerplus, Vol. 3, No. 4, pp. 666-678, 2015.
- Thripurna Thatipelli and Padmavathi Kora, “Classification of Myocardial Infarction using Discrete Wavelet Transform and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 429-432, 2014.
- M. Durairaj and S. Sivagowry, “A Pragmatic Approach of Preprocessing the Data Set for Heart Disease Prediction”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 11, pp. 6457-6465, 2014.
- S. Florence, N.G. Bhuvaneswari Amma, G. Annapoorani and K. Malathi, “Predicting the Risk of Heart Attacks using Neural Network and Decision Tree”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 11, pp. 7025-7030, 2014.
- Li Xiang-Wei and Qi Yian-Fang, “A Data Preprocessing Algorithm for Classification Model based on Rough Sets”, Physics Procedia, Vol. 25, pp. 2025-2029, 2012.
- V.V. Jaya Rama Krishniah, D.V. Chandra Sekar and K. Ramchand H Rao, “Predicting the Heart Attack Symptoms using Biomedical Data Mining Techniques”, The International Journal of Computer Science and Applications, Vol. 1, No. 3, pp. 10-18, 2012.
- V. Kumutha and S. Palaniammal, “An Enchanced Approach on Handling Missing Values using Bagging K-NN Imputation”, Proceedings of International Conference on Computer Communication and Informatics, pp. 123-128, 2013.
- Tahani Aljuaid and Sreela Sasi, “Proper Imputation Techniques for Missing Values in Data sets”, Proceedings of International Conference on Data Science and Engineering, pp. 108-116, 2016.
- S. Oba, I. Takemasa, M. Monden and K. Matsubara, “A Bayesian Missing Value Estimation Method for Gene Expression Profile Data”, Bioinformatics, Vol. 19, No. 16, pp. 2088-2096, 2003.
- Influence of Plant Growth Regulators on Yield and Quality Characters of Brinjal (Solanum melongena L.) Cv.ANNAMALAI
Abstract Views :311 |
PDF Views:0
Authors
Affiliations
1 Department of Horticulture, Faculty of Agriculture, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
1 Department of Horticulture, Faculty of Agriculture, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
Source
The Asian Journal of Horticulture, Vol 13, No 2 (2018), Pagination: 45-49Abstract
A field experiment was carried out to study the effect of plant growth regulators on growth and yield of brinjal cv. ANNAMALAI. The growth regulators were applied in three different concentrations viz., NAA (25, 50 and 100 ppm), GA3 (50, 100 and 200 ppm) and ethrel (50, 100 and 200 ppm). The experiment was laid out in Randomized Block Design (RBD) with ten treatments and three replications. The earliness in flowering, number of flowers, fruit set percentage, fruit length, fruit girth, fruit weight and yield per plant were registered in the plants sprayed with GA3 @ 50 ppm. The fruits obtained from the plants that were sprayed with GA3 @ 200 ppm also recorded highest total soluble solids and ascorbic acid content, which ultimately had better taste than the other treatments. Among the growth regulators tested, GA3 @ 50 ppm was found to produce best results in improving the growth and yield of brinjal cv. ANNAMALAI.Keywords
Brinjal, NAA, GA3, Yield Characters.References
- Arora, I., Singh, J.P. and Singh, R.K. (2014). Effect of concentration and methods of application of 2,4-D and NAA on plant growth, flowering, yield and quality in summer season chilli (Capsicum annum L.) cv. PANTC-1. Adv. Res. J. Crop Improve., 5(2):176-180.
- Athaneria, M.K., Sengar, N. and Pandey, B.R. (2011). Influence of biofertilizer on growth and yield of chilli.Veg. Sci., 38(1):101103.
- Desai, S.S, Chovatia, R.S. and Singh, V. (2012). Effect of different plant growth regulators and micronutrients on fruit characters and yield of tomato cv. GUJARAT TOMATO-3 (GT3). Asian J. Hort., 7(2): 546-549.
- Gavaskar, D. and Anburani, A. (2004). Influence of plant growth regulators on flowering and fruit yield in brinjal (Solanum melongena L.) cv. ANNAMALAI. South Indian J. Hort., 52 (1/6): 135-138.
- Gollagi, S.G., Hiremath, S.M. and Chetti, M.B. (2006). Influence of growth regulators and nutrients on morphological traits and yield potential in chilli cv. BYADAGI. J. Asian Hort., 2(3): 182-185.
- Gowda, P.M., Shrivashankar, K.T. and Mathai, P.J. (1986). Principles of vegetable production: Plant growth substances in vegetable production. Vegetable crops in India Naya Prakash Calcutta-6, pp. 62-65.
- Hiffny, I.M.M and Sayed, M.A.M. (2011). Response of sweet pepper plant growth and productivity to application of ascorbic acid and biofertilizers under saline conditions. Australian J. Basic & Applied Sci., 5(6):1273-1283.
- Jayaram, K.M. and Neelakandan, N. (2000). Effect of plant growth regulators on sex determination in Solanum melongena Linn. Indian J. Plant Physiol., 5(3): 288-289.
- Joshi, N.C. and Singh, D.K. (2001). Effect of plant bioregulators on growth and yield of chilli (Capsicum annuum L.). Prog. Hort., 35(2): 212-215.
- Kalshyam, M.K., Kumar, Jitendra, Mohan, Braj, Singh, J.P. and Rajbeer, Nathi Ram (2012). Effect of plant growth hormone and fertilizer on growth and yield parameters in chilli (Capsicum annum L.) cv. PUSA JWALA. Anna. Hort., 5(1): 140-143.
- Khan, Salauddin and Pariari, Anupam (2013). Effect of plant growth regulators on chilli. Res. Crops, 14(1) : 235-236.
- Khurana, D.S., Manchanda, Dimple, Singh, Jaswinder and Singh, Kulbir (2004). Influence of napthalene acetic acid on growth and fruit yield of chilli. Haryana J. Hort. Sci., 33 (3/4): 274-275.
- Krishnamurthi, S. and Subramanian, D. (1954). Some investigations on the types of flowers in brinjal (Solanum melongena L.) based on style length and their fruit set under natural conditions and in response to 2,4-D as a plant growth regulator. Indian J. Hort., 11 : 63-67.
- Lodhi, S.K., Rathore, S.V.S., Verma, M.S., Rana, P. and Prakash, S. (2002). Effect of plant spacing and gibberellic acid on the performance of chillies (Capsicum frutescens Linn.) cv. PUSA SADABAHAR (for ripe-red fruits). Prog. Agric., 2(1): 82-83.
- Lone, M.T., Haripriya, K. and Maheswari, T.U. (2005). Influence of plant growth regulators on growth and yield of chilli (Capsicum annuum L.) cv. K 2.Crop Res., 29 (1): 111-113.
- Meena, R.S. (2008). Effect of GA3 and NAA on growth, yield and quality of tomato (Lycopersicon esculentum Mill.) cv. PUSA RUBY grown under semi-arid conditions. Curr. Agric., 32 (1/2): 83-86.
- Meena, S.S. and Dhaka, R.S. (2003). Effect of plant growth regulators on growth and yield of brinjal under semi-arid conditions of Rajasthan. Ann. Agric. Res., 24 (3): 516-521.
- Panse, V.G. and Sukhatme, P.V. (1967). Statistical methods for agricultural workers. ICAR. Publ., New Delhi. pp. 381.
- Patel, V.P., Plal, E. and John, S. (2016). Comparative study of the effect of plant growth regulators on growth, yield and physiological attributes of chilli (Capsicum annum L.) cv. KASHIANMOL. Internat. J. Farm Sci., 6(1):199-204.
- Shahid, M.R., Amjad, M., Ziaf, K., Jahangir, M.M., Ahmad, S., Iqbal, Q. and Nawaz, A. (2011). Growth, yield and seed production of okra as influenced by different growth regulators. Pakistan J. Agric. Sci., 50 (3) : 387- 392.
- Shantappa Tirakannanavar, A.M.S., Ahmed, P.M., Munikrishnappa, L., Chavan, Mukesh and Mastiholi, A.B. (2009). Effect of growth regulators and methods of application on growth, fruit and seed yield in paprika chilli (Capsicum annuum L.) cv.KT-P1-19. Seed Res., 37 (1/2): 14-19.
- Singh, B.K. Abhishek, Singh, A.K. and Rai, V.K. (2011a). Varietal response of NAA on growth and yield of brinjal (Solanum melongena L.). Environ. & Ecol., 29 (3): 1036-1038.
- Singh, B.K., Vivek Kumar, A.K. Singh and Rai, V.K. (2011b). Role of NAA on growth, yield and quality of tomato (Lycopersicon esculentum Mill.) cultivars. Environ. Ecol., 29 (3): 1091-1093.
- Singh, K.V., Singh, B. and Mohan, Braj (2010). Response of growth regulators on growth and yield of chilli (Capsicum annuum L.). Prog. Agric., 10 (1) : 200-201.
- Sorte, P.N., Damke, M.M., Rafeekher, M., Goramnagar, H.B. and Bobade, P.M. (2001). Influence of GA and IAA on growth, yield and fruit quality of different varieties of brinjal. J. Soils & Crops, 11(1): 128-131.
- Thapa, U., Pati, M.K., Chattopadhay, S.B., Chattopadhyay, N. and Sharangi, A.B. (2003). Effect of growth regulators on growth and seed yield of chilli (Capsicum annuum L.). J. Interacademicia., 7 (2): 151-154.
- Thompson, C. and Kelly, William C. (1957). Vegetable crops. Tata Mc Graw Publications, pp.143.
- Veishnav, B.K., Neeraj, Singh, A.K. and Singh, S.P. (2012). Effect of NAA on growth and yield of chilli (Capsicum annuum L.). Environ. Ecol., 30(4): 1261-1263.
- NHB (2015). Statistical databases for horticultural crops. www.nhb.co.in.
- Influence of Plant Growth Regulators on Growth Characters of Brinjal (Solanum melongena L.) Cv.ANNAMALAI
Abstract Views :194 |
PDF Views:0
Authors
Affiliations
1 Department of Horticulture, Faculty of Agriculture, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
1 Department of Horticulture, Faculty of Agriculture, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
Source
The Asian Journal of Horticulture, Vol 13, No 2 (2018), Pagination: 59-63Abstract
A field experiment was carried out to study the effect of plant growth regulators on growth and yield of brinjal cv. ANNAMALAI. The growth regulators were applied in three different concentrations viz., NAA (25, 50 and 100 ppm), GA3 (50, 100 and 200 ppm) and ethrel (50, 100 and 200 ppm). The experiment was laid out in Randomized Block Design (RBD) with ten treatments and three replications. The results of this experiment revealed that the plants that was sprayed with GA3 @ 200 ppm evinced better performance for the production of plant height, number of primary branches, number of secondary branches, number of leaves, leaf area and leaf area index.Keywords
Brinjal, NAA, GA3, Growth Characters.References
- Balraj, R., Kurdikeri, M.B. and Revanappa (2002). Effect of growth regulators on growth and yield of chilli (Capsicum annum L.) at different pickings. Indian J. Hort., 59(1): 84-88.
- Gavaskar, D. and Anburani, A. (2004). Influence of plant growth regulators on flowering and fruit yield in brinjal (Solanum melongena L.) cv. ANNAMALAI. South Indian J. Hort., 52 (1/6): 135-138.
- Gollagi, S.G., Hiremath, S.M. and Chetti, M.B. (2006). Influence of growth regulators and nutrients on morphological traits and yield potential in chilli cv. BYADAGI. J. Asian Hort., 2(3): 182-185.
- Gowda, P.M., Shrivashankar, K.T. and Mathai, P.J. (1986). Principles of vegetable production: Plant growth substances in vegetable production. Vegetable crops in India Naya Prakash Calcutta-6, pp. 62-65.
- Kalshyam, M.K., Kumar, Jitendra, Mohan, Braj, Singh, J.P. and Rajbeer, Nathi Ram (2012). Effect of plant growth hormone and fertilizer on growth and yield parameters in chilli (Capsicum annum L.) cv. PUSA JWALA. Annals Hort., 5 (1): 140-143.
- Khan, Salauddin and Pariari, Anupam (2013). Effect of plant growth regulators on chilli. Res. Crops, 14(1) : 235-236.
- Khurana, D.S., Manchanda, Dimple, Singh, Jaswinder and Singh, Kulbir (2004). Influence of napthalene acetic acid on growth and fruit yield of chilli. Haryana J. Hort. Sci., 33 (3/4): 274-275.
- Lone, M.T., Haripriya, K. and Maheswari, T.U. (2005). Influence of plant growth regulators on growth and yield of chilli (Capsicum annuum L.) cv. K 2.Crop Res., 29 (1): 111-113.
- Meena, R.S. (2008). Effect of GA3 and NAA on growth, yield and quality of tomato (Lycopersicon esculentum Mill.) cv. PUSA RUBY grown under semi-arid conditions. Curr. Agric., 32 (1/2): 83-86.
- Meena, S.S. and Dhaka, R.S. (2003). Effect of plant growth regulators on growth and yield of brinjal under semi-arid conditions of Rajasthan. Annal. Agric. Res., 24(3): 516-521.
- Panse, V.G. and Sukhatme, P.V. (1967). Statistical methods for agricultural workers. ICAR. Publ., New Delhi. pp. 381.
- Revanappa Nalawadi, U.G. and Chetti, M.B. (1998). Influence of growth regulators on ischolar_main growth, flowering and yield of green chilli. Karnataka J. Agric. Sci., 11 (4): 1009-1013.
- Shantappa Tirakannanavar, A.M.S., Ahmed, P.M., Munikrishnappa, L. , Chavan, Mukesh and Mastiholi, A.B. (2009). Effect of growth regulators and methods of application on growth, fruit and seed yield in paprika chilli (Capsicum annuum L.) cv.KT-P1-19. Seed Res., 37 (1/2): 14-19.
- Singh, B.K., Kumar, Vivek, Singh, A.K. and Rai, V.K. (2011b). Role of NAA on growth, yield and quality of tomato (Lycopersicon esculentum Mill.) cultivars. Environ. Ecol., 29(3): 1091-1093.
- Singh, K.V., Singh, B. and Mohan, Braj (2010). Response of growth regulators on growth and yield of chilli (Capsicum annuumL.). Prog. Agri., 10 (1): 200-201.
- Sorte, P.N., Damke, M.M., Rafeekher, M., Goramnagar, H.B. and Bobade, P.M. (2001). Influence of GA and IAA on growth, yield and fruit quality of different varieties of brinjal. J. Soils & Crops, 11(1): 128-131.
- Thapa, U., Pati, M.K., Chattopadhay, S.B., Chattopadhyay, N. and Sharangi, A.B. (2003). Effect of growth regulators on growth and seed yield of chilli (Capsicum annuum L.). J. Interacademicia, 7 (2): 151-154.
- Thompson, C. and Kelly, William C. (1957). Vegetable crops. Tata Mc Graw Publications, pp.143
- Veishnav, B.K., Neeraj, Singh, A.K. and Singh, S.P. (2012). Effect of NAA on growth and yield of chilli (Capsicum annuum L.). Environ. Ecol., 30(4): 1261-1263.
- NHB (2015). Statistical databases for horticultural crops. www.nhb.co.in.