Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
- Title
- Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
- Creator
- Shanthini K.S.; Francis J.; George S.N.; George S.; Devassy B.M.
- Description
- The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd
- Source
- Food Control, Vol-167
- Date
- 2025-01-01
- Publisher
- Elsevier Ltd
- Subject
- Bruise level classification and prediction; Early bruise detection; Hyperspectral image (HSI); Strawberry
- Coverage
- Shanthini K.S., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; Francis J., Department of Computer Science, Christ University, Karnataka, Bangalore, India; George S.N., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; George S., Department of Computer Science, Norwegian University of Science and Technology, Gjik, Norway; Devassy B.M., Department of Computer Science, Norwegian University of Science and Technology, Gjik, Norway
- Rights
- Restricted Access
- Relation
- ISSN: 9567135; CODEN: FOOCE
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Shanthini K.S.; Francis J.; George S.N.; George S.; Devassy B.M., “Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/12567.