Smart Crop Recommendation: Fusing Nutrient and Climate Data with Krill Herd Optimization and Explainable AI

The dataset used in this system was obtained from Kaggle and combines rainfall, climate, and nutrient data from Indian sources. It includes 2200 instances with 11 features: Nitrogen, Potassium, Phosphorus, Copper, Iron, Magnesium, Sulphur, Temperature (°C), Rainfall (mm), Humidity (%), and pH. The output consists of 22 crop categories: Pigeon peas, Chickpea, Coffee, Pomegranate, Kidney beans, Apple, Muskmelon, Rice, Black gram, Cotton, Maize, Coconut, Grapes, Moth beans, Banana, Jute, Watermelon, Mung beans, Papaya, Lentil, Orange, and Mango, with around 100 records per crop.