Machine vision approach for classification of citrus leaves using fused features

Abstract

The objective of this study was to observe the potential of machine vision (MV) approach for the classification of eight citrus varieties. The leaf images of eight citrus varieties that were grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri. These were acquired by a digital camera in an open environment without any complex laboratory setup. The acquired digital images dataset was transformed into the multifeature dataset that was the combination of binary, histogram, texture, spectral, rotational, scalability and translational (RST) invariant features. For each citrus leaf image, total 57 multi-features were acquired on every non-overlapping region of interest (ROI), i.e. (32x32), (64x64), (128x128), and (256x256). Furthermore, the optimized 15 features using the supervised correlation-based feature selection (CFS) technique were acquired. The optimized multi-features dataset to different MV classifiers namely Multilayer Perceptron (MLP), Random Forest (RF), J48 and Naïve Bayes using10-fold cross-validation method were plugged-in. The results produced by MLP presented an average overall accuracy of 98.14% on ROIs (256x256) outperforming the other classifiers. The classification accuracy values by MLP on the eight citrus leaf varieties,

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

“…Lastly, the comparative analysis performs for the classification of medicinal plant leaves with the sizes of ROO's 220 × 220 and 280 × 280, respectively, as shown in Figure 11. The methodology proposed is comparatively reliable and efficient from that described previously [13,14,[16][17][18][19][20]. Furthermore, it is consistent, satisfactory, and better from the existing medicinal plant leaves classification.…”

“…Image processing algorithms are used to identify the leaf images [11][12][13][14][15][16][17][18][19][20]. The background behind this claim is developed below.…”

“…Finally, they achieved a 93.5% cumulative classification result. Reference [20] proposed a leaf-based classification of citrus plants. They used digital images and extracted 57 multi-features datasets, then optimized these features into 15 features via the ML approach.…”

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

“…In comparison to the proposed deep learning methodology, the performance of machine learning classifiers is very limited in terms of different measures like precision recall and f1measure. On the citrus dataset, Qadri et al [23] reported an accuracy of 82.91 percent for KNN, but when we experimented, we received accuracy results for KNN=65.84 percent (see Table 10).…”

“…The proposed CNN model is compared to Qadri et al [23]'s machine learning-based work. In comparison to the proposed deep learning methodology, the performance of machine learning classifiers is very limited in terms of different measures like precision recall and f1measure.…”