Open AccessShape and texture based classification of citrus using principal component analysis
Author(s) -
Naeem Akhtar,
Muhammad Idrees,
Furqan ur Rehman,
Muhammad Ilyas,
Qaiser Abbas,
Muhammad Luqman
Publication year - 2021
Publication title -
international journal of agricultural extension
Language(s) - English
Resource type - Journals
eISSN - 2311-8547
pISSN - 2311-6110
DOI - 10.33687/ijae.009.02.2525
Subject(s) - principal component analysis , computer science , usable , reliability (semiconductor) , artificial intelligence , linear discriminant analysis , texture (cosmology) , pattern recognition (psychology) , image (mathematics) , power (physics) , physics , quantum mechanics , world wide web
Citrus family consists of a variety of eatable, consumable and usable items with varying nutritional contents. Naked eye citrus classification needs expert human effort, which provides poor decision reliability. The unreliable classification decision may be extremely hazardous when the citrus is being classified for exports or usage in pharmacy products and various food items. In this paper, citrus fruit has been classified on shape and texture features. Principal Component Analysis (PCA) was used as a methodology to explore statistical findings. The average accuracy of the system proposed is 84%. This system can be implemented on pharmacy stores, food production units, or industries, and citrus export centers for reliable citrus fruit classification.