AgriSense: Crop Advisor

Advanced software in regenerative farming assists cultivators in managing crop information, such as cycle, reaping, and soil management. Sensors assess soil conditions. This information aids in developing a system that provides crop suggestions based on variables like crop specifics, soil makeup, climate, temperature, soil acidity, and precipitation. Machine learning models, such as Logistic Regression, Random Forest, and Decision Tree, were utilized on data from the Indian Chamber of Food and Agriculture on Kaggle. Findings highlight that PyTorch Tabular Fit yields the most accurate results at 99% precision and faster model construction.