Abstract: The process of soil analysis involves assessing various parameters to understand the quality and composition of the soil. This includes evaluating nutrient content, moisture levels, texture among other factors. The aim is to gather comprehensive information about the soil's characteristics, allowing for informed recommendations on treatments to improve fertility and optimize agricultural practices. To further automate and optimize agricultural practices, the system incorporates a moisture sensor. This sensor measures the moisture levels in the soil and assesses the need for water. When the system determines that additional moisture is required, it automatically triggers the release of these resources (water) into the field. To enhance this soil management system, Yolo v8 algorithm are employed. This algorithm play a crucial role in identifying and classifying unwanted plants within the crop field. By leveraging advanced image recognition and analysis, the system can distinguish between desired crops and invasive or harmful plants. Once identified, the system promptly notifies users, enabling timely intervention to address these issues and maintain the health of the crops. In addition to plant identification, the system provides real-time weather updates to users. This feature ensures that farmers and stakeholders are informed about current and upcoming weather conditions.
Keywords: Soil analysis, Yolo algorithm, Real Time Weather Updates, Moisture Sensor, Automation for Agricultural Practices
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DOI:
10.17148/IJARCCE.2024.134185
