Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

Browse

Filters

2019 - 201912020 - 20212

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Smart Agriculture

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationEmbargo
    Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
    (IEEE, 2021-10-27) Gamage, R; Rajapaksa, H; Sangeeth, A; Hemachandra, G; Wijekoon, J; Nawinna, D. P
    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.
  • Thumbnail Image
    PublicationEmbargo
    Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
    (IEEE, 2021-10-27) Gamage, R; Rajapaksa, H; Sangeeth, A; Hemachandra, G; Wijekoon, J; Nawinna, D
    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.
  • Thumbnail Image
    PublicationEmbargo
    Autonomous cloud robotic system for smart agriculture
    (IEEE, 2019-07-03) Dharmasena, T; De Silva, R; Abhayasingha, N; Abeygunawardhana, P. W. K
    Agriculture sector occupies 25.9% of the world employment. The demand for food production is rapidly increasing with the increase of world population. Developing the existing agricultural infrastructure by incorporating modern technologies will help to match this increasing demand. This paper proposes a automated system to optimally control the climate and irrigation in a greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect the unhealthy plants using image processing. A fuzzy controller will control the heating and cooling system, irrigation system and humidifiers installed in the greenhouse based on the sensor readings. The mobile robot navigates through a predefined map of the greenhouse and collect soil samples to perform measurements while onboard sensors will collect the ambient climate data. A camera mounted on the mobile robot will capture the plant and detect unhealthy crops based on the colour and the texture of the leaves.