Open AccessDisorder detection of tomato plant(solanum lycopersicum) using IoT and machine learning
Author(s) -
Saiqa Khan,
Meera Narvekar
Publication year - 2020
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 - 10.1088/1742-6596/1432/1/012086
Subject(s) - scope (computer science) , yield (engineering) , cloud computing , computer science , artificial intelligence , internet of things , modal , machine learning , agriculture , agricultural engineering , embedded system , engineering , chemistry , biology , operating system , ecology , materials science , polymer chemistry , metallurgy , programming language
India is an agricultural country and this sector accounts for 18 percent of India’s GDP. This sector is the backbone of the country and focuses on better yield by using pesticides and fertilizers to prevent plant disorders which directly affects the yield. The primary method adopted for detecting disorders is through visual observation and other methods are quite expensive. Many authors have proposed solutions to this problem such as IoT for grapes, or system designed for accurate disorder detection using machine learning with limited scope. This paper showcases a prototype that uses multi-modal analysis through sensor data, computer vision. The main objective of this system is to accurately detect disorders in tomato plant using IoT, Machine Learning, Cloud Computing, and Image Processing.