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Research Article | Open Access
Volume 13 2021 | None
RASPBERRY PI-BASED CROP PREDICTION SYSTEM
Dr .Y Srinivasulu, Rps Raju, L Sunitha, Kancharla Harshitha
Pages: 3151-3162
Abstract
Agriculture is the major source for the largest population in India to earn money and carry out their livelihood. Precision agriculture is already adopted in other countries, but we still need to involve IoT and cloud computing technologies for better production of crops. At present the climate differs in many areas around India due to various factors from human activities such as air pollution, deforestation, sewage and from natural changes such as distance of sea, wind direction, proximity to the equator. As per the changes in the climate, a farmer needs to predict which crop should be cultivated at which time. The dataset stores the details of the crop which should satisfy the requirements such as maximum and minimum temperature, maximum and minimum rainfall, soil type and location. The current temperature and rainfall range data can be collected by using DHT11 Temperature Sensor and Soil Moisture Sensor connected to Raspberry Pi. The collected data's (location, temperature value and rainfall range) are stored in AWS IoT. Connections with remote locations can easily achieved by using messaging protocol such as MQTT (Message Queue Telemetry Transport). The publish-subscribe pattern requires a message broker. The broker is responsible for distributing messages to interested clients based on the topic of a message. The Decision trees are versatile Machine Learning algorithm that can perform both classification and regression tasks in predicting the crop to be cultivated in a corresponding location as per the climatic changes. Amazon QuickSight helps to visualize the data by comparing with the trained data.
Keywords
Agriculture is the major source for the largest population in India to earn money and carry out their livelihood.
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