Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
SMART AGRO
is a web-based platform developed to revolutionize agricultural production,
distribution, and marketing management in Sri Lanka, with a primary focus on
paddy production. This platform utilizes various DL and ML technologies, such
as RNN, AI, CNN, ANN, and LSTM to provide advanced functionalities. One of the
key features of SMART AGRO is its paddy demand forecasting module. This module
leverages historical harvest data from previous years to predict future demand.
By analysing trends and patterns in the data, the platform can provide accurate
forecasts, aiding farmers and stakeholders in making informed decisions
regarding production and distribution. Another important aspect of SMART AGRO
is its cost estimation functionality. This feature utilizes past costing data
to estimate the expenses associated with paddy production. By considering
factors as labor, equipment, fertilizers, and other inputs, the platform can
provide farmers with a comprehensive cost estimate for their production
activities. To enhance security and user access control, SMART AGRO implements
multifactor authentication with face recognition. This ensures that only
authorized users can access the paddy demand forecasting and cost estimation
functions of the platform. Additionally, SMART AGRO includes a communication
platform that facilitates knowledge sharing among users. This platform utilizes
AI chat capabilities to enable users to interact with the system and seek
information or assistance. Moreover, an email server is employed for
knowledge-sharing purposes, allowing users to exchange information, documents,
and best practices related to agricultural practices. By combining advanced
technologies like DL and ML with features such as demand forecasting, cost
estimation, and knowledge sharing, SMART AGRO aims to optimize agricultural
practices, improve decision-making processes, and enhance productivity within
the Sri Lankan paddy farming industry.
Country : Sri Lanka
IRJIET, Volume 7, Issue 10, October 2023 pp. 523-531