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@article{204440,
        author = {Venkatesh B N and Dr Lokesh A and Dr.krishna Kumar P R},
        title = {Soil Fertility Analysis and Digital Mapping Using the SCORPAN Model and Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2805-2829},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204440},
        abstract = {Fertility of the ground shapes how well farms produce, plus affects long-term care of landscapes. Checking it the old way means gathering many samples out in fields, sending them off for testing - slow work, expensive too. Now tools like digital soil models, satellite imagery, smart algorithms, and geographic software help sketch out dirt traits across wide regions without boots on every acre. Starting with how dirt behaves, this method ties together seven things: the ground itself, weather patterns, living stuff around it, hills and valleys, what rock layers lie beneath, how old everything is, plus where exactly it sits on Earth. Instead of just listing them, it looks at rain levels, heat changes, how people use land, plant health signs, height above sea level, steepness of terrain, underground materials, and exact map points. These details help spot links between earth makeup and surroundings. Not every factor acts alone; each shifts influence depending on location. Patterns emerge when data connects across regions. Prediction grows clearer by combining these pieces carefully. Location shapes outcomes more than expected. Relationships form based on real-world overlaps seen over time. What happens above affects below in measurable ways. Hidden trends appear through consistent measurement. Mapping becomes possible once connections settle into place. Out of fields come patterns when models like Random Forest dig into soil traits - Nitrogen, Phosphorus, Potassium, alongside Organic Carbon and pH. Where elevation data meets weather records, clarity forms through layers stacked in GIS software. From space shots to ground truth, mapping life beneath boots begins with pixels shaped by climate archives and terrain shapes. Out in the fields, knowing where soil thrives comes down to smart mapping - this method nails those details fast. Instead of guessing, machines learn patterns from landscape clues like rainfall, terrain, and parent material, turning data into clear pictures of fertile ground. Farmers might spot weak spots before planting, simply by reading these color-coded layouts. Even planners find value when shaping long-term land strategies without digging endless test pits. Accuracy climbs once algorithms join classic soil science, swapping old guesswork for sharper forecasts. Decisions about crops gain clarity because each zone tells its own story through numbers turned visible. Less walking across acres collecting samples means more time acting on solid insights.},
        keywords = {},
        month = {June},
        }