Identification of Oily Cinnamon Leaves Using Image Processing (CinnaOil)

Abstract

The oiliness of cinnamon leaves, a crucial component in assessing their eligibility for essential oil extraction, is still evaluated by the Sri Lankan cinnamon industry using conventional techniques, despite technological developments. This outdated method results in inconsistent evaluations, lower oil output, and unpredictable product quality. These challenges limit the cinnamon oil industry's long term growth and efficiency in addition to affecting present production. In order to get over these restrictions, this study presents an AI powered method that precisely determines the oiliness of cinnamon leaves by utilizing machine learning and image processing approaches. The VisionTransformImageProcessor model is used by the CinaOil system to extract relevant visual information from images of cinnamon leaves. To improve performance and deal with dataset imbalance, advanced training techniques such early stopping model checkpointing, class weighting, and learning rate scheduling were used. The accuracy and generalization of the model were further improved by background removal and other preprocessing techniques. Reliable predictions in real world applications were ensured by the model's great stability over a range of environmental circumstances and image modifications. Confusion matrices and classification reports were used to assess CinaOil's performance, and it showed excellent accuracy in identifying between oily and non oily leaves. The model demonstrated high precision and recall with a test accuracy of 98.1%, demonstrating its efficacy in determining the best leaves for oil extraction. A CNN-based model and an AI- enhanced model were the two separate components that were developed and evaluated. The AI model was used in the final application because it performed better than the CNN model, which had an accuracy of 93.6%, with 98.1%. The goal of this scalable, user friendly program is to assist cinnamon producers by offering a reliable and automated way to evaluate quality, and ultimately helping in the modernization of the cinnamon sector.