Rice plants disease identification with mobile application.

Disease damage to rice can greatly reduce yield. Farmer loses an estimated average of 37% for diseases. For that reason we came up with the idea of helping the farmers to detect the diseases at the initial stage and we will also attempt to provide them with necessary control measures.

We are attempting to develop a system that will detect the diseases of rice plants through image processing where Farmers will take snapshots of the leaves/stems of their disease affected crops and send them to the system server where the image will go through several levels of processing to detect and identify the disease.

This simple system implementation will allow people with very little technological knowledge especially rural people to use and get effective help out of it. The barrier between the rural people and the technology will exist no more if we try to utilize the benefits of technology.

Rice is the staple food of about 135 million people of Bangladesh. It provides nearly 48% of rural employment, about two-third of total calorie supply and about one-half of the total protein intake of an average person in the country. Rice sector contributes one-half of the agricultural GDP and one-sixth of the national income in Bangladesh.

Almost all of the 13 million farm families of the country grow rice. Rice is grown on about 10.5 million hectares which has remained almost stable over the past three decades. About 75% of the total cropped area and over 80% of the total irrigated area is planted to rice. Thus, rice plays a vital role in the livelihood of the people of Bangladesh. Disease damage to rice can greatly reduce yield. Farmer loses an estimated average of 37% for diseases. For that reason we came up with the idea of helping the farmers to detect the diseases at the initial stage and we will also attempt to provide them with necessary control measures.

Our desire to serve the nation as much as possible. In Bangladesh the agriculture mainly role the wheel of the economy so serving the farmers is serving the nation as of our understanding. The leading newspapers of the country often publish the news regarding the problem faced by the paddy production industry and our main target is to solve the problem faced by the farmers who are cultivating paddy by making them an easy way to find the disease of rice plant at the earlier stage of the plant and to indicate the probable solution to overcome the situation.

In our research, we have implemented an automated system for disease detection of rice plants using image analysis and machine learning. We have also integrated this system with a mobile application for Android phones to serve the farmers where they get benefited by identifying the diseases correctly and taking measures accordingly.  At first, an image of the defected rice plant has been taken with the camera of the mobile phone. The farmers then just have to send the image to our server by selecting the options provided on the application.The image has been analyzed in the server where the plant’s affected parts will be segmented using our predefined segmentation method, features for the texture analysis has been extracted using color co-occurrence methodology and comparing with our pre-defined database the disease is identified and classified using , features for the texture analysis has been extracted using color co-occurrence methodology and comparing with our pre-defined database the disease is identified and classified using SVM classifierclassifier.

In our research we do lot of background study.our technology included with the technical issue and exiting system like this.But everywhere we notice that our system will be the new the,because our system will complete the task without human interaction.we are going to use image processing and machine learning for detection disease.our system will be able to sent identification report and necessary measures without human interaction.

some exiting application which are working with this criteria ,but all are less similar to our project.

1:Rice Doctor

2: RiceXpert

3: Rice Knowledge Bank

• Digitalis farming system.
• Reduce damage in rice cultivation.
• Helping the rural farmer.
• Increase overall rice production.
• Save money.

Two person:

1.Tusar Kanti Das.

2.Debbroto Debadhikary.

We have plan to implement our system for real life application in the rural areas so that the people of our country get benefited by our work and it will help them in rice cultivation immensely.We want to develop Bangla language layout and Bangla voice navigation for our mobile application in near future.

Fee-for-service model