• Kochumol Abraham
• Dr. J P K Meenakshisundaram
• Win Mathew John,
• Sijo Thomas

Most people in India are farmers. They depend on agriculture. They have aim to increase productivity and quality of product. Vegetables and fruits are the most important agricultural products for from customer view. The economical profit depends on a product quality which is depends on a quality of a soil, seeds and fertilizers. So for increasing the profit farmer mainly focuses on these three main things. Instead there is one more thing which effect on a production that is diseases. To increase profit we have to control these diseases. But it is necessary to detect and control such diseases in a specific period which is at their initial state. These diseases occur due to the pathogens such as fungi, bacteria and viruses, and due to adverse environmental conditions. Therefore, it is necessary to diagnosis a plant disease. For that farmers requires continuous monitor the plant body which is time consuming process. It also the very expensive process for the farmers. So the latest develop method give us machine view for detecting plant diseases which is much accurate and less time consuming. Computer vision and machine learning have the capability of resolving this issue and enabling accurate, scalable high-throughput phenotyping. Among machine learning approaches, deep learning has emerged as one of the most effective techniques in various fields of modern science. The proposed system is used to record environmental conditions, pest’s detection and identify the factors which lead to changes in tea leaves. Extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. The primary focus is on providing real-time data acquisition, analysis and monitoring solutions for plant breeders and researchers to boost the productivity. For small scale farmers, early identification of disease is very much possible and able to control the insects by organic pesticides or by the use of minimal amount of chemical pesticides. For large scale farmers frequent monitoring and early identification of disease is not possible and it results in a severe outbreak of the disease and pest growth which cannot be controlled by organic means. In this situation farmers are forced to use the poisonous chemicals to eradicate the disease in order to retain the crop yield. This problem can be solved by automating the monitoring process by use of advanced image processing techniques.

Machine Learning, Segmentation, Deep Learning

"Prediction of stress phenotypes in tea plants using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.691-696, December-2018, Available :http://www.jetir.org/papers/JETIR1812398.pdf

"Prediction of stress phenotypes in tea plants using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp691-696, December-2018, Available at : http://www.jetir.org/papers/JETIR1812398.pdf