Abstract: India being an agriculture country, its economy predominantly depends on agriculture yield growth and agro industry products. Maintaining a high yield is a very important issue in agriculture. Any farmer is interested in knowing how much yield he is about to expect. By analyzing the various related attributes like location, pH value from which alkalinity of the soil can be determined. Along with this, percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K) Location can be used alongside the use of third-party apps like APIs, weather and temperature, type of soil, nutrient value of the soil in that region, quantity of rainfall, soil composition can also be determined. All these attributes of data will be analyzed, train the data with various suitable machine learning algorithms for creating a model. The system comes with a model to be precise and accurate in guiding the end user with proper recommendations about required fertilizer ratio based on atmospheric and soil parameters of the land which enhance to increase the crop yield and increase farmer revenue. It also suggests a gel, oil, or any other chemical agent that would help crops grow better. Furthermore, the system will use crop yield history data to fine-tune the predictions and recommendations. Integrating satellite imagery will enable the model to analyze vegetation health and detect issues before they occur. The inclusion of real-time sensor data from IoT devices on the farm will enhance the accuracy of the predictions. The system will also consider pest and disease predictions so that appropriate pre-emergent preventative actions and readiness notification are received in time. Moreover, it will provide advice to clients for the best times for plantings under seasonal weather regimes.

Keywords: K- Nearest Neighbor, Support Vector Machine, Machine learning, Efficient Farming, System Architecture.

| DOI: 10.17148/IARJSET.2025.12106