Crops Disease Diagnosis

Project Title

Crops Disease Diagnosis

Project Area of Specialization Shared EconomyProject Summary

Agriculture plays a crucial role in the economy of Pakistan. It contributes 21% to the GDP (Gross Domestic Product) of Pakistan. About 62% rural population relates to agriculture for their livelihood and 44% of labor force is engaged to it. Agriculture is playing a vital role in contributing to the economic sector from three different perspectives; food provision, employment, and foreign exchange earnings.It is obvious from the above that how much it is important to secure our future in agriculture. For this purpose, this study paid more attention to the crops, their rapid and better growth. We are minimizing the factors that are limiting their proper growth. In this project we research on the crop diseases with the use of visual detection techniques and developed a Web-Based Application that can help the farmers, researchers and experts to provide disease diagnose and possible cure for the betterment of crops. To increase the growth and productivity of crop field, the automatic monitoring of disease is more accurate instead of manual detection methods. Manual monitoring method does not gives satisfactory results and are quiet time taking, so, modern technique used for disease detection to get accurate results. In this research, we used K-means clustering technique for detecting multiple diseases in multiple crops and introduced a web based tool to help farmers to identify the diseases of the strawberry, blackberry, rose plant, tomato and potato. An input image given by the user that goes through different processing steps to detect the diseased leaf and to show it’s results. The main target of research was to provide an efficient implemented method of disease detection for the formers.

Project Objectives

The objectives of our project is to develop a web application that use Single technique for identifiction of multiple diseases of different  crops, Only color based diseases will be detected to achieve high efficiency and K-Mean is used for accuracy.

Project Implementation Method

We started our research with setting aims and objectives of our research to help farmers in a best way. About 40 articles were selected and from those we separated the articles related to visual disease detection. These articles were further divided according to different techniques and algorithms. On the basis of those articles we documented a literature review. Then we comparatively studied all the techniques used in previous work and critically analyze them to select most effective technique. We selected k-mean as it provides high accuracy and implemented it on color diseases of five crops. We selected 2 to 3 diseases of each crops. We implement this technique to get different solutions on different crops. On the basis of solution the results are deducted.

Benefits of the Project

This research has demonstrated the usefulness of implementing a visual detection technique on a web based application to providing accuracy and efficiency at the same time. It is remotly accessable which is helpful for farmer to use it anywhere any time. It reduces the cost by mimizing the usage of resouces.

Technical Details of Final Deliverable

this application is developed by using web languages HTML, CSS, JavaScript and PHP. For identification of disease user input an image which goes through different  processing steps. In first step image is uploaded, then image is processed to remove noise to enhance the iamge, Segmentation is performed on enhanced image using k-Means clustering algorithm to grouping the pixels on color basis. Then these clusters are classified using if-THEN rules to identify disease.

Final Deliverable of the Project Software SystemType of Industry IT Technologies Shared EconomySustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
Publication	Equipment	2	35000	70000
Data set	Miscellaneous	1	10000	10000