Identification of Mango Leaves by Using Artificial Intelligence

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  • Imran MAQBOOL
  • Salman QADRI
  • Dost Muhammad KHAN
  • Muhammad FAHAD


Mango Leaf, Morphological Feature, classification, artificial neural network (ANN), CVIPTools


This research presents a classification based novel artificial intelligence approach of Mango Leaves recognition. The design and
implementation of an artificial neural network system that extracts specific shape and morphological features from mango plant leaves of
three kinds is presented in this research. Modules of significant mango leaf image features are identified using a novel feature selection
technique. This technique reduces the dimensionality of the feature space leading to a simplified classification and identification scheme
appropriate for real time classification systems for better results. In making the system complete, a full account is given of the necessary
image processing methods that are applied to the binary images of mango plant leaves to ensure identification. These methods include the
extraction of shapes from binary images.The proposed method inherits size and orientation invariance with respect to the image datasets and it can operate successfully even with leaves samples that are deformed due to dropout or due a number of holes drilled in them. A considerably very high classification ratio
of 96% to 98% was achieved, even for the identification of deformed leaves.




How to Cite

MAQBOOL, I., QADRI, S., KHAN, D. M., & FAHAD, M. (2019). Identification of Mango Leaves by Using Artificial Intelligence. International Journal of Natural and Engineering Sciences, 9(3), 45–53. Retrieved from




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