Identification of Plants leaf Diseases using Machine Learning Algorithms\

  • Monalisa Saha, E. Sasikala

Abstract

The innovative growth of a country depends on agribusiness. Mother of all cultures agriculture provides food and raw materials. It is very important for human as a source of eating material. So plant diseases become very big problem for human being. Plant diseases can be happened any time. It can be happened between sowing and harvesting. It is a big loss in the economic value of the market. So that leaf disease detection plays a big role in agriculture. So detecting disease used traditional methods. But the traditional method used for leaf disease detection is empty eye observation by agriculture experts or plants pathologist. The detection of plant leaf diseases, using this method, which may be subjective, time-taken, cost-effective, required huge manpower, required extensive knowledge about plant diseases. Using experimentally evaluate software solution it can automatically detect and classify plant leaves diseases. The machine learning is used for new evolution. Machine learning used for detecting diseases on plants. Machine learning is one of the sub part of Artificial Intelligence to work automatically or give instructions to do a particular work. The main aim of machine learning is to understand the training data and fit that training data into models that should be useful to the people. So we can use machine learning to detect diseases in plants. It has been assisting good decisions making and predicting the large amount of data produced. The color of leaves, amount of damage to leaves, area of the unhealthy plant leaf are used in classification. In this we overviewed different machine learning algorithms to identifying different plant leaves diseases and identifying the best accuracy.

Published
2020-05-14
How to Cite
Monalisa Saha, E. Sasikala. (2020). Identification of Plants leaf Diseases using Machine Learning Algorithms\. International Journal of Advanced Science and Technology, 29(9s), 2900 - 2910. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/15512