diseased wheat plant detection using drone

Project Title

diseased wheat plant detection using drone

Project Area of Specialization Artificial IntelligenceProject Summary

Our project is consists of two modules one is flying module and the other one is ground module. In flying module we have a quadcopter (Drone) which will capture a picture from the top of a wheat crops and then send it to the ground module(Data base) that we have made based on image processing which will classify the diseased area of the crops and the healthy one. When you want to find which disease is available in the crops then you should to take the diseased wheat plat leaf picture and send to the database that will classify which disease is there in the crops and which chemical can be used for it. In our case we have to identify the four major diseases in the wheats by just giving the picture of crops to the database.  

Project Objectives

Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product.Health monitoring and disease detection on plant is very critical for sustainable agriculture.It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require excessive processing time. Hence, we use image processing for the detection of plant diseases. 
Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. our main objective is to provide a system to every farmer to prevent thier crops from properly because it is easy to use. As we know agriculture is directly link with economy of every country so we take the advantages of image processing techniques in the agriculture side.

Project Implementation Method

project implementation is very simple. We have to take picture of diseaded plant and pass through the different steps involved in the database and the the database will store the features of that defective leaf picture.In this way our database will get trained and it will be just one time. Now when you  want to examine any crops just capure the picture and send to the database it will clarify that the crops is defective or effective.but when you to find that which area of the crops is defective just fly your drone and take picture from the top of the crop and then send to the system,it will highlight the defective area and then you have to apply chemical on that specific diseased area of crops.

Benefits of the Project

The existing method for plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases is done. For doing so, a large team of experts as well as continuous monitoring of plant is required, which costs very high when we do with large farms. At the same time, in some countries, farmers do not have proper facilities or even idea that they can contact to experts. Due to which consulting experts even cost high as well as time consuming too. In such conditions, the suggested technique proves to be beneficial in monitoring large fields of crops. Automatic detection of the diseases by just seeing the symptoms on the plant leaves makes it easier as well as cheaper. 

Plant disease identification by visual way is more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take less efforts, less time and become more accurate. In plants, some general diseases seen are brown and yellow spots, early and late scroch, and others are fungal, viral and bacterial diseases. Image processing is used for measuring affected area of disease and to determine the difference in the color of the affected area

Technical Details of Final Deliverable

our project has to do main two tasks.First it give the result of the disease available in the crops and the other one to give the result of defective area in  the crop and also give suggestions about the pestiside have to apply on the defective area of the crop.

Final Deliverable of the Project HW/SW integrated systemType of Industry Agriculture Technologies Artificial Intelligence(AI)Sustainable Development Goals Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
Drone camera	Equipment	1	50000	50000
arduino	Equipment	2	1000	2000
ethernet shield	Equipment	2	1500	3000
andriod phone	Equipment	1	10000	10000
penaflix,travel,and jumper wires etc	Miscellaneous	1	5000	5000