Objective of our project is to deliver a computer vision and artificial intelligence based solution which will automatically detect the disease in apples using Azure machine learning for building data models. The quality and yield of fruits can be degraded too much in the presence of the diseases in the fruit. Manual distinguishing proof of infected fruits is tedious. The utilization of image processing procedures is of outstanding implication for the analysis of agro based applications.
No such system exist in the market that can help people and agriculture industry to automatically detect disease infected apples.
Through IDE called ML studio, we can build data models through drag-and-drop gestures and simple data flow diagrams. This not only minimizes coding, but also saves a lot of time through ML Studio's library of sample experiments.
In our idea three normal diseases of apple fruit are considered i.e. Apple scab, Apple rot and Apple blotch. The image processing based proposed methodology will be used to extract color, texture and shape based features. The primary steps of the introduced image processing-based method are as follows:(1) infected fruit part detection is done with the help of K-means clustering method,(2) color-, texture- and shape-based features are computed over the segmented image and combined to form the single descriptor, and (3) multi-class support vector machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with three categories of diseases, namely blotch, rot and scab as well as healthy apples.
ABOUT THE ENTRANT
Type of entry:teamTeam members:yashaswi gautam
Software used for this entry:MATLAB