Open AccessIdentification Of Ripe And Unripe Citrus Fruits Using Artificial Neural Network
Author(s) -
Rex Fiona,
Shreya Thomas,
Isabel J. Maria,
Beverly Hannah
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1362/1/012033
Subject(s) - agriculture , image processing , yield (engineering) , maturity (psychological) , identification (biology) , artificial neural network , computer science , agricultural engineering , sorting , food processing , grading (engineering) , artificial intelligence , image (mathematics) , engineering , geography , biology , botany , food science , psychology , developmental psychology , materials science , civil engineering , archaeology , metallurgy , programming language
Agriculture has a major role in the economic development of our country. Productive growth and high yield production of fruits is essential and required for the agricultural industry. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. Image processing techniques can be used to reduce the time consumption and has made it cost efficient. In this paper, we have provided a survey to address these challenges using image processing techniques.