Development of a computer vision based Eggplant grading system

A comprehensive review of fruit and vegetable classification techniques

“…The Gabor filter used can extract specific frequency components that can be used for segmentation [32,108]. Recently, Otsu based segmentation has been used for fruit and vegetable defect detection and a common limitation of holes generation for similar intensity level as background has been identified [39,40,57,81]. A combination of LBP, HOG, global colour and shape feature has been used with Otsu thresholding for optimal ROI selection in a multi-class fruit recognition and identified to be improved for effective results [7].…”

“…A non-exhaustive description of shape, texture and colour feature descriptors has been described in this section. 1996 Mixed Classification Threshold-based pixel level image subtraction [66] 2006 Apple Quality assessment Feature-based with variable neighbourhood size [30] 2006 Citrus Quality assessment Texture based HSI and Colour Co-occurrence (CCM) [31] 2007 Apple Quality assessment Gabor kernel and PCS avoided local features segmentation [32,108] 2012 Mixed fruit Fruit harvesting Spatial-local adaptive threshold based [52] 2012 Mixed Classification Distance Transform (DT) and watershed [77] 2013 Vege Detection Texture and edge fusion segmentation [78] 2015 Mixed fruit Detection K-mean split and graph-based merge with area threshold [110] 2016 Apple Recognition Dynamic threshold Otsu method [111] 2016 Mixed fruit Classification Square window split and merge segmentation [72] 2016 Tomato Quality assessment Otsu method [39] 2017 Apple Bruise detection HSI based Otsu method [40] 2017 Eggplant Grading Intensity adaptive threshold based Otsu [57] 2018 Apple Detection Graph based k-mean FCM clustering [14] 2018 Litchi Robotic harvesting One dimensional random signal histogram with FCM [59] 2018 Mixed fruit Detection Fusion of LBP, HOG, global colour and shape with Otsu [7] 2018 Packed food Quality assessment 3 × 3 patch likelihood threshold with CNN [109] 2018 Papaya Disease detection K-mean clustering based segmentation [44] 2018 Pomegranate Clustering Threshold Otsu [81] Ability to recognise and retrieve from partial information.…”

Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network

“…This approach is different from classification methods in general which actually reduce the initial dimensions to simplify the computational process to provide better prediction accuracy. The Kernel functions that are usually used in SVM are Linear (10), Polynomial (11), Radial Basis Function (12), Tangent Hyperbolic or Sigmoid (13), and Inverse Multiquadratic (14).…”

“…1 (2023) DOI: https://doi.org/10.29207/resti.v7i1.4715 Creative Commons Attribution 4.0 International License (CC BY 4.0) 169 the Random Forest (RF) for predicting papaya ripeness demonstrated an accuracy of 94.7% [12]. Furthermore, K-Nearest Neighbor (KNN) for interest recognition demonstrated 90% accuracy [13], Egg quality assessment demonstrated an accuracy of 88% [14], and 1-NN for vegetable classification demonstrated 80% accuracy [15]. Support Vector Machine (SVM) for Mango scoring demonstrated 100% accuracy [16], while Grapevine detection demonstrated 97.70% accuracy [17].…”

“…2017 [9] 94.94 Apel fruits, ANN (classifier), Grey Level Co-occurrence Matrix (GLCM), K-means (clustering segmentation). 2017 [14] 88% 50 Eggplant images, KNN (classifier), Otsu 2017 [17] 97.70 760 Grapevine images, SVM (classifier), features transform and bag of features. 2017 [19] 95.6 26 categories, CNN (classifier), VGG (pre-trained).…”