International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Smart Farming: A Machine Learning and IoT Approach Mrs.B. Veena1, P. Sathyapal Reddy2, M.K.Sai Srikar3, M.Shilpa4 1Assistant Professor, Institute of Aeronautical Engineering, Hyderabad, Telangana.
234Student of Electronics and Communication Engineering, Institute of Aeronautical Engineering,
Hyderabad, Telangana. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Many prime varieties of foods come from
improve sustainability, while also reducing the environmental impact of agriculture. However, implementing smart farming practices can be a complex and costly process. It requires significant investment in technology, data analytics, and infrastructure. Additionally, there may be challenges related to data privacy and security, as well as the need to train farmers and other stakeholders on how to use these technologies effectively. Despite these challenges, the potential benefits of smart farming are significant, and many farmers and agricultural companies are already embracing this approach. With continued advancements in machine learning and IoT technologies, the future of agriculture looks bright, with the promise of increased efficiency, productivity, and sustainability.
agricultural lands. Food is the most important means of survival and is a necessity for every creature. In India, there are a lot of agricultural lands and a lot more farmers who produce a wide variety of crops. There are a lot of unfortunate cases where farmers just couldn't bear the loss of a crop be it due to rainfall, droughts, or fertilizer faults. In this project, we propose a way for agriculture to be made fruitful and lush. This is done by implementing an IoT module that uses many different sensors like temperature, pH, moisture, and even rainfall sensors which detect the changes in these particular factors and process the data onto a Machine learning model. An Algorithm is then used to process and make decisions based on which irrigation is provided to the crops. This method can help to conserve water as well as give the freedom of manipulating the module using an app that controls the module and informs the farmer about it. This method also helps in healthier crop production while being cost-effective.
1.1 Block diagram Raspberry Pi is used as the main IoT module to which multiple sensors are connected to obtain the required data as shown in fig1. Moisture sensor is used to obtain information about the wetness present in the soil and will result in switching on the irrigation system incase it is dry. DHT11 sensor is used to measure the temperature and humidity present in the soil and will respond via accordingly incase the threshhold temperature goes up. Rainfall sensor is sensitive to changes in weather and will be active while there is rainfall. PIR sensor is mainly useful in detecting animals / movement via infrared rays and can help in notifying the breach to the owner. Finally all the data is sent via a hotspot to the cloud where the data can be updated in realtime.
Key Words: IoT, Machine learning, Random-forest, Raspberry pi Model 3B, Passive infrared Sensor (PIR), Rainfall sensor, Soil Moisture Sensor, Temperature Sensor (DHT11).
1. INTRODUCTION This Smart farming, also known as precision agriculture, is an emerging approach that uses upcoming technologies such as machine learning and the internet of things (IoT) to increase the efficiency and productivity of agriculture. By integrating digital technologies into agriculture, farmers can monitor and optimize the use of resources such as water, fertilizer, and pesticides, while also improving crop yield and quality. Machine learning algorithms are used to analyze data collected from various sources such as sensors, weather stations, and drones. These algorithms can identify patterns and provide insights that help farmers make informed decisions about when to plant, irrigate, and harvest their crops. In addition, IoT devices such as soil moisture sensors, temperature sensors, and smart irrigation systems can be used to automate and optimize the farming process. Smart farming has the potential to address many of the challenges facing the agriculture industry, such as food insecurity, climate change, and resource depletion. It can help farmers reduce waste, increase efficiency, and
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