Machine Learning-Based Crop Prediction: A Way Towards Smart Farming

The provision of a certain percentage of domestic goods to ensure food security makes agriculture essential to the Indian economy. Since it contains essential nutrients, the soil is the most crucial component for flourishing agriculture. However, due to abnormal climatic changes, food production and prediction are now becoming diminished, which will have a significant impact on farmers’ economies. Temperature, moisture, and other weather patterns are also important factors in enhancing soil nutrients because they are associated with the mechanisms of photosynthesis, seedling growth, and saturation. The major goal of this endeavour is to develop a web-based GUI for a crop forecast model based on Machine Learning Operations (MLOps). To help farmers by directing them to plant suitable crops, the suggested method can be utilised to assess the soil nutrient concentration by accessing the associative characteristics including the environmental parameters. XGBoost, a tree-focused ensemble machine learning algorithm, outperformed other classification algorithms by obtaining an accuracy of 99.318%.