Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

Ensuring accurate classification of rice as either cooked or dry is vital for food safety, as improperly stored or cooked rice contain harmful bacteria, emphasizing the importance of maintaining and monitoring food safety standards. In the field of image analysis and food classification, classifying dry and cooked rice samples using photographs is an interesting but difficult task. The main challenge stems from the minute visual variations between cooked and dry rice, which has not always displayed distinct traits that are easily observable by machines. Hence, various machine learning algorithms were implemented to effectively mitigate this issue. However, the existing works have not analysed the physicochemical characteristics due to non-destructive type of experimentation method with image processing. To overcome this issue, this work develops the Classification and Regression Tree (CART) of Decision Tree Learning method for classifying the rice grain samples as dry or cooked based on the physicochemical characteristics such as morphological, texture and color features, which in turn gain an exhaustive facts of rice quality in diverse state. Initially, the images are captured, pre-processed and the features are extracted. From the extracted features, the rice samples are classified as dry and cooked using DT and the results are compared with the existing algorithms like K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). The comparative analysis of these classification algorithms infers the outperformance of the DT learning model under morphological, texture and color features in terms of accuracy, error, precision, recall and F-score.