Deep Learning Model Optimization for Crop Prediction and Recommendation using Harris Hawks Optimization

The dataset comprises 2,200 entries with 7 key attributes, covering both soil and climate factors. Soil attributes include pH, nitrogen, phosphorus, and potassium, while climate factors include temperature, rainfall, and humidity. It features data for 22 crops, such as watermelon, coconut, lentil, rice, cotton, grapes, muskmelon, black gram, coffee, banana, apple, orange, moth beans, chickpea, kidney beans, mango, pigeon peas, papaya, jute, pomegranate, mung bean, and maize, with around 100 records per crop.

本数据集共计包含2200条数据条目，涵盖7项关键属性，覆盖土壤与气候两类核心影响因子。其中土壤属性包括pH值、氮、磷、钾；气候因子则包含温度、降雨量与空气湿度。本数据集覆盖22种栽培作物，具体包括西瓜、椰子、小扁豆、水稻、棉花、葡萄、甜瓜、黑绿豆、咖啡、香蕉、苹果、橙子、蛾豆、鹰嘴豆、芸豆、芒果、木豆、木瓜、黄麻、石榴、绿豆及玉米，每种作物对应约100条数据记录。