Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment
Tanilal Bhavsingh Thakur1, Amit Kumar Mittal2
1Tanilal Bhavsingh Thakur, Senior lecturer, Computer Science Department, Govt Polytechnic College Barwani (MP), India.
2Amit Kumar Mittal, Assistant Professor, Department of Computer Engineering, Institute of Engineering Technology, DAVV Indore (MP), India.
Manuscript received on December 25, 2019. | Revised Manuscript received on January 04, 2020. | Manuscript published on January 15, 2020. | PP: 1-4 | Volume-6, Issue-4, January 2020. | Retrieval Number: D1186016420/2020©BEIESP | DOI: 10.35940/ijisme.D1186.016420
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: India is an agricultural country. A total of 61.5% of the people cultivate in India. Due to lack of agricultural land and change of weather, many types of diseases occur on crops and insects are born. Therefore, the production of crops is coming down. To reduce this problem, Internet of Things technology will prove to be an important role. In this system, a sensor network will be created on agricultural land using Raspberry Pi 3 model. The images of the crops will be taken by sensor cameras and these images will be sent to the cloud server via Raspberry Pi 3 model. In this proposed methodology, various image processing techniques will be apply on acquired images for classification of crop diseases using k-means clustering algorithm with unsupervised machine learning. This paper will also shows the method of image processing technique such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases. In bad natural environment, the farmers can produce quality crops and people will get healthy food by this proposed methodology and make more profit. In real time treatment of crop diseases, farmer will increase quantity of their crops.
Keywords: Crop Diseases, Image Processing, Internet of Things, Machine Learning, Raspberry Pi 3 and Sensors.