Overview / Usage

The project aims to revolutionize agricultural practices by harnessing technology to address key challenges in crop selection. By utilizing sensors and microcontrollers, we can collect accurate and real-time data on soil and moisture levels. This data is crucial for farmers to make informed decisions about which crop varieties to plant, optimizing yield and resource utilization. Moreover, the integration of these technologies facilitates automation and precision agriculture, leading to increased efficiency and sustainability in food production. Overall, this work has the potential to significantly impact agricultural practices, promoting more resilient and productive farming systems.

Methodology / Approach

Our methodology centers on harnessing technology to optimize agricultural practices, particularly in crop selection. We adopt a systematic approach that involves deploying sensors to gather essential data on soil moisture levels, nutrient content, and environmental factors. This data is then processed and analyzed using microcontrollers, such as the ESP32, leveraging algorithms for data interpretation.

Key frameworks and standards, such as Arduino IDE for microcontroller programming, ensure compatibility and ease of development. Additionally, we adhere to industry standards for sensor calibration and data validation, ensuring the accuracy and reliability of our measurements.

Techniques like signal processing and machine learning algorithms are employed to extract actionable insights from the collected data, aiding in precise crop recommendations. Moreover, user-friendly interfaces are prioritized to enhance accessibility and usability for farmers.

By integrating cutting-edge technology with established methodologies and standards, our approach empowers farmers to make data-driven decisions, ultimately optimizing crop selection and improving agricultural productivity and sustainability.

Technologies Used

In the development of this project, a comprehensive array of technologies, tools, and hardware were employed:

Software:

Arduino IDE: Provides an intuitive platform for programming microcontrollers. It offers a user-friendly interface and a rich set of features for writing, compiling, and uploading code to the microcontroller.

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Libraries and Frameworks:

ESP32 Arduino Core: This core provides libraries and tools specifically tailored for programming ESP32 microcontrollers. It simplifies the development process by offering a comprehensive set of functionalities for data acquisition, processing, and communication.

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Hardware:

ESP32 microcontroller: This powerful microcontroller facilitates data acquisition, processing, and communication tasks. It offers high processing power and ample memory, making it suitable for handling complex data analysis and decision-making tasks.

TDS (Total Dissolved Solids) sensor: This sensor plays a crucial role in measuring soil nutrient levels, providing essential data for assessing soil quality and crop health.

Soil moisture sensor: This sensor monitors soil moisture content, a critical parameter for efficient irrigation management and crop growth optimization.

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Intel Technologies:

**Intel Edison or Intel Joule: **These Intel platforms offer higher processing power and additional computational capabilities, making them suitable for handling advanced data analysis and decision-making tasks. If the project requires intensive computation or sophisticated algorithms, these platforms could be considered for enhancing performance and efficiency.

Repository

https://github.com/Yuvasreesivakumar/Crop-Suggestor