Sustainable Coffee Cultivation Expansion using IT Innovations
Volume 6 - Issue 7, July 2023 Edition
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Priyankara D M U, Senanayake S M R M, Manoramya H A R, Gunawardena W A T R, Ms.Lokesha Prasadini K M , Pradeepa Senani Bandara
Sri Lanka, coffee cultivation, IT technologies, machine learning, forecasting, image processing, IoT, Edge detection, CNN algorithms, Linear regression.
Expansion of Sri Lankan Coffee Cultivation Using New IT Technologies Coffee cultivation in Sri Lanka faces various challenges such as unpredictable weather, low yield, and diseases. To address these challenges, this research explores the use of new IT technologies to expand Sri Lankan coffee cultivation. The research is a combination of four aspects. determine the most suitable coffee variant to plant in a given location in Sri Lanka and predict yield based on weather factors. A machine learning-based web application is developed to analyze soil type, temperature, precipitation, and elevation using GPS coordinates. From This approach, the application provides farmers with recommendations on best practices for planting and cultivation, based on the analyzed data and predictions. predict future export coffee flavor demand and price by using a machine learning model, that used sales historical data and other economic factors which affected. Machine learning techniques are used to create an image processing-based system that accurately detects and diagnoses minor ailments in coffee plants. The system examines photographs of coffee plants and provides suggestions for the management and treatment of the problems found. It also concentrates on figuring out the best amount of additional fertilizer needed for production and selecting the coffee type best suited for a certain soil texture. The decisions made regarding upcoming supply strategies and marketing tactics will be based on these anticipated demand levels. An IOT-based system is developed to collect real-time data on soil texture and nutrient levels using sensors and provides predictions on the most suitable coffee variety and the optimal amount of additional fertilizer required for cultivation. By combining edge detection and CNN algorithm operations, illnesses in coffee plants may be accurately identified. With the aid of these activities, precise input data for illness detection can be provided. In order to estimate consumer demand for coffee flavor, gradient, and long-term memory techniques are also used, which improves forecast accuracy. In addition, linear regression is used to forecast production and choose the best coffee type. Overall, this research proposes new IT-based solutions to expand Sri Lankan coffee cultivation by addressing challenges related to weather factors, flavor demand, plant diseases, and soil characteristics. The proposed solutions for large-scale coffee producers and exporters have the potential to improve the productivity, profitability, and sustainability of Sri Lankan coffee cultivation.
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