IOT BASED DISEASE DETECTION AND PREDICTION OF POMEGRANATE LEAF
Abstract
Agriculture is one of the important parts of human life. Precision farming is very essential in today’s world. In the process of production of plant food, leaf plays an important role in the growth of plant. Diseases on leaf affects on the food of the plants and quality of the product. Geological condition is extraordinary for farming in light of the fact that it gives numerous good conditions .We have to detect the disease of leaf but it is difficult task to monitor the whole farm. Now a day the use of IOT is increased rapidly. In IOT domain we can collect data from different devices. We can overcome this problem by using the automatic leaf disease detection using IOT. By applying image processing we can easily work on different types of images. By collecting, the information from various types of sensors predicts the diseases that can be affect the leaf. In this thesis we have used four different sensors. 1. PH Sensor, 2. Temperature sensor, 3.Humidity sensor, 4.Soil Moisture Sensor.
We can collect data through Raspberry pi as well as collect plant images through camera. The main goal of the proposed work is to monitor the plant leaf, detect and classify them according to the diseases using the data mining and image processing techniques. By collecting, the information from various types of sensors predicts the diseases that can affect the leaf. We have implemented the classification and clustering algorithm to sort out good quality and bad quality plant detection. Image is first captured from farm of plant leaf and then it passes to further Image processing. Image pre-processing to be does on the acquired images. Image segmentation is used for segmentation of plant leave images and lastly features extracted for detection of diseases for the classification and classification done using the SVM classifier. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 400 images with an accuracy of 90%.
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Under the Creative Commons Attribution- 4.0 International License (CC BY-4.0 DEED).
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
