Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

The purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. A digital camera captured these images of guava varieties in a natural environment. Multi-features were extracted from these images. It was a composite of histograms, binary features, textures, rotational, spectral, and translational features (RST). Total 47 multi-features were collected for each non-overlapping guava leaf image, i.e., 256 x 256 and 512 x 512 more, the supervised correlation-based feature selection (CFS) method with the best search algorithm was used to optimize 18 features instead of 47 multi-features. Several ML classifiers, including Instant base Identifier (IBI), Random Forest (RF), and Meta Bagging (MB), using 10-fold cross-validation, were applied to the optimized multi-features. IBI results performed better than other classifiers with an average overall accuracy of 93.01% on AOIs, 512 x 512 . In addition, IBI detected 90.5%, 89.5%, 94%, 97%, 95.5%, 97%, 99%, 96.5%, 99%, 80.5%, 88%, and 81.5% accuracy values for the 12 varieties of guava leaves, namely Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. The proposed study could play a significant role for the early and accurate identification of Guava varieties, and it would also be helpful for export quality measures for the national economy of the country.

Automated summary

The purpose of this study was to explore the potential of Machine Vision (MV) approaches in the classification and identification of 12 varieties of guava. Leaf images of the 12 guava varieties were captured using a digital camera and various features were extracted. The experiment involved the optimization of features and the use of multiple machine learning classifiers, with Instant base Identifier (IBI) performing the best. This study plays an important role in the early and accurate identification of guava varieties and in formulating export quality measures for the national economy.