Paper

Year

Objectives

Algorithms

Data used

Results

[21]

2012

Automatic detection and grading of mango fruit according to size, color and skin of the mango.

Fuzzy image clustering algorithm

Dataset of self-collected mango images.

80%

[20]

2013

Grading of mango, grape and pomegranate affected by anthracnose.

Thresholding, region growing, K-means clustering and watershed + ANN classifier

Dataset of 600 fruits’ image samples collected by a color Digital Camera.

Normal fruits: 84.65% affected fruit: 76.6%

[18]

2016

Automated grading of mango (Mangifera Indica L.) according to maturity and quality.

SVR + MADM

Mango video images captured by a CCD camera.

87%

[12]

2017

Mango diseases recognition.

K-Means Clustering + MRKT + ANN

500 images of mango leaves and fruits from a Nikon 16 MP digital camera.

98%

[13]

2017

Mango diseases recognition.

K-Means Clustering + MRKT + ANN + Wavelet-PCA

500 images of mango leaves and fruits from a Nikon 16 MP digital camera.

Flower: 98.50%; Leaf: 98.70%; Fruit: 98.75%

[19]

2017

Defect identification and maturity detection of mango fruits.

Proposed image processing algorithms

Dataset of 28 mango images, including 14 defected mangos and 14 did not defected others.

Not specified

[8]

2018

Mango Leaf Ailment (disease) Detection (MLAD).

ANN (Neural Network Ensemble) + SVM

Own dataset.

Mean accuracy of 80%

[14]

2018

Mango leaf unhealthy region detection and classification.

K-means + GLCM and Multiclass SVM

The dataset contains images divided into 5 classes as follows: 61 images for anthracnose, 50 for red rust, 55 for black rot, 75 for scab and 45 for healthy mango leaf.

96%

[11]

2020

Mango leaf disease detection and identification.

Neural Network Ensemble (SVM) + K-means clustering

Self-collected images.

Average accuracy of 80%

[16]

2021

Mango leaf disease detection and identification.

CCA + cubic SVM

29 self-collected RGB images using different types of gadgets and from different areas. 135 images after pre-processing.

95.50%