Open AccessComputer Vision Based Weed Removal System using Object Detection based on Convolutional Neural Network
Author(s) -
Pramod M Kanjalkar,
Jyoti B Kulkarni,
Parth A Basole
Publication year - 2022
Publication title -
ymer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.103
H-Index - 5
ISSN - 0044-0477
DOI - 10.37896/ymer21.01/50
Subject(s) - computer science , convolutional neural network , artificial intelligence , task (project management) , agriculture , weed , deep learning , object (grammar) , digital camera , object detection , agricultural engineering , computer vision , pattern recognition (psychology) , engineering , geography , agronomy , systems engineering , archaeology , biology
Agriculture is one of the oldest and certainly most important professions in history of humankind. Agriculture itself is embedded deep within our foundation of country. Due to ever growing demand and growth of technology, it is possible to help farmers to boost their production via some means of current technology that is may be via robotics. Weeding and harvesting in particular area due to large land side requires repetition of monotonous task. It does not help in resulting much efficiently for weeding i.e. (using excessive herbicide and margin for human error). There is also a lack of manpower noticeable in agriculture sector. To detect and remove weed from other crops is essential task for the farmers. Hence, the aim of the proposed system is to detect weed from the other crops in the images captured by digital camera. This system uses an object detection technique by Convolutional Neural Network. The proposed system is very helpful in agriculture area