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Jeyalakshmi, S.
- Utility of Community Based Nutritional Rehabilitation Programme
Abstract Views :198 |
PDF Views:0
Authors
Affiliations
1 RUHSA Department, Christian Medical College and Hospital RUHSA Campus P.O. 632 209 North Arcot Ambedkar District, Tamil Nadu, IN
1 RUHSA Department, Christian Medical College and Hospital RUHSA Campus P.O. 632 209 North Arcot Ambedkar District, Tamil Nadu, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 34, No 8 (1997), Pagination: 203-207Abstract
Hunger and malnutrition perhaps are the most wide spread human suffering in the world today. Malnutrition affects the physical and mental well being and learning behaviour of the children. Poverty and ignorance are the major factors responsible for this high rate of malnutrition observed in the developing countries.
- A Review on Diagnosis of Nutrient Deficiency Symptoms in Plant Leaf Image Using Digital Image Processing
Abstract Views :492 |
PDF Views:9
Authors
S. Jeyalakshmi
1,
R. Radha
2
Affiliations
1 Department of Computer Science, Guru Nanak College, IN
2 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IN
1 Department of Computer Science, Guru Nanak College, IN
2 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 4 (2017), Pagination: 1515-1524Abstract
Plants, for their growth and survival, need 13 mineral nutrients. Toxicity or deficiency in any one or more of these nutrients affects the growth of plant and may even cause the destruction of the plant. Hence, a constant monitoring system for tracking the nutrient status in plants becomes essential for increase in production as well as quality of yield. A diagnostic system using digital image processing would diagnose the deficiency symptoms much earlier than human eyes could recognize. This will enable the farmers to adopt appropriate remedial action in time. This paper focuses on the review of work using image processing techniques for diagnosing nutrient deficiency in plants.Keywords
Color Segmentation, Color Space, Mathematical Morphology, Color Feature Extraction, Classifier.References
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- Classification of Tomato Diseases using Ensemble Learning
Abstract Views :223 |
PDF Views:1
Authors
S. Jeyalakshmi
1,
R. Radha
2
Affiliations
1 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women
2 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IN
1 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women
2 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2408-2415Abstract
A Plant disease is any dysfunction of a plant, caused by living organisms, which affects the quality and quantity of yield. These symptoms are visually shown on the plant leaves. This paper discusses classification of Tomato diseases such as Late Blight, Septoria Leaf Spot and Yellow leaf curl virus while distinguishing the healthy leaf at the same time. An experimental sample size of 1817 was considered in conducting this study. This work differentiates diseased tomato leaf images with healthy leaf images. The classifiers Random Forest, Multilayer Perceptron Neural Network and Support Vector Machines were trained and got a prediction accuracy of 88.74%, 89.84%, and 92.86% respectively in classifying diseases. Then, the prediction results of Random Forest, Multilayer Perceptron and Support Vector Machines were combined using Soft Voting classifier and obtained a highest accuracy of 93.13% in classifying tomato diseases.Keywords
Tomato Diseases, Support Vector Machines, Multilayer Perceptron, Random Forest, Voting Classifier.References
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