Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1299
Title: Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
Authors: Gamage, R
Rajapaksa, H
Sangeeth, A
Hemachandra, G
Wijekoon, J
Nawinna, D
Keywords: Smart Agriculture
Prediction System
Vegetables Grown
Sri Lanka
Issue Date: 27-Oct-2021
Publisher: IEEE
Citation: R. Gamage, H. Rajapaksa, A. Sangeeth, G. Hemachandra, J. Wijekoon and D. Nawinna, "Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka," 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021, pp. 0246-0251, doi: 10.1109/IEMCON53756.2021.9623259.
Series/Report no.: 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON);Pages 0246-0251
Abstract: Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm.
URI: http://rda.sliit.lk/handle/123456789/1299
ISSN: 2644-3163
Appears in Collections:Department of Computer systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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