IMAGE PROCESSING AND DATA MINING MONITORING IN THE PREDICTING DISEASE OF LEAF USING ANDROID APPLICATION

Computer Science and Engineering

India is fast developing country and agriculture is the back bone for the countries development in the early stages. Due to industrialization and globalization concepts the field is facing hurdles. On top of that the awareness and the necessity of the cultivation need to be instilled in the minds of the younger generation. Now a day’s technology plays vital role in all the fields but till today we are using some old methodologies in agriculture. Identifying plant disease wrongly leads to huge loss of yield, time, money and quality of product. Identifying the condition of plant plays an important role for successful cultivation. Identification of plant disease is very difficult in agriculture field. Leaf disease detection requires huge amount of work, knowledge in the plant diseases, and also require the more processing time. The objective of this research is to make use of significant features and prediction is done using computer vision technique. This method mainly download the image from the server then it converts the image into a gray scale by calculating its pixels and it shows out only the defected parts of the leaf. Generally we can observe the symptoms of disease on leafs, stems, flowers. so here we are using the leafs for identification of disease affected plants. First we need to select the plant which is affected by the disease and then collect the leaf of the plant and take a snapshot of leaf and load the leaf image into the system. It means representation of the image in more meaningful and easy to analyze way. In segmentation, a digital image is partitioned into multiple segments can defined as super-pixels. The main objective of this project is to find out whether the leaf is defected or not. If the leaf is in green color, then there is no defect in the leaf. If the leaf is spotted with black dots then it is shown to be affected by some disease and then we are predicting the disease of leaf and its lifetime is also too low. This approach can significantly support an accurate detection of leaf disease. We can extend this approach by using image processing technique. It displays the output in graphical view that is X and Y coordinates. The user can also view the output in mobile application by retrieving the result from the server.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.