Image processing is a strategy to play out a few operations on a picture, keeping in mind the end goal to get an upgraded picture/image or to separate some helpful data from it. It is a sort of signal processing in which input is an image and yield might be image or attributes/highlights related to that image.
Cardamom maturity detection is a unique system built to detect the stages of maturity of cardamom using advanced image processing technology. A sample of cardamom is taken from a bulk, an image is captured with the help of a webcam and it is analyzed with software in PC/Laptop. The software is developed with certain color matching algorithms. A standard color for the different stage of maturity is recorded by consulting with the experts and is fed to the software thereafter the by matching the standard color with the sample color the software can analyze the maturity of the cardamoms.
Grading and classification of Cardamom are based on observations and through experiences. The system utilizes image-processing techniques to classify and grade quality of Cardamom. Two dimensional images are classified on shape and color based analysis methods. However, different cardamom images may have similar or identical color and shape values. Hence, using color or shape features analysis methods are still not effective enough to identify and distinguish cardamom images. Therefore, it is used as a method to increase the accuracy of the cardamom quality detection by using color, shape, and size based method with a combination of Artificial Neural Network (ANN). ANN method grades and classifies cardamom images based on obtained feature values by using the cascaded forward network. The system starts the process by capturing the cardamom’s image. Then, the image is transmitted to the processing level where the features like color, shape and size of cardamom samples are extracted. After that by using artificial neural network fruit images are going through the training and testing.