Smart Irrigation System By Recommending Crop And Fertilizers

A key component of precision agriculture is crop recommendation, which strives to maximize the harvest by taking into account ecological and soil variables. Utilizing light Gradient Boosting Machine (LGBM) algorithm intrinsic simplicity and clarity, we put forward an ensemble-based approach for crop recommendation in this work. The algorithm leverages a record of past crop yield and soil type to account for a number of parameters, including pH, soil nutrient levels, and weather patterns. According to our insights, this LGBM algorithm is a useful tool that generates precise and coherent crop suggestions, which supports productive and organic farming techniques. Using a random forest approach, this study attempts to anticipate fertilizer and assist farmers in making educated decisions on auxiliary fertilizer use. For a given set of input parameters, the model predicts the ideal type and quantity of fertilizer needed by using a random forest method. The random forest approach is precise and helpful in recommending fertilizer with precision, which supports resource-efficient and sustainable farming practices, as evidenced by experiments.