Stacked Ensemble Model for Accurate Crop Yield Prediction Using Machine Learning Techniques

We used historical data for crop yield in 27 Indian states and 3 Union Territories of India, covering the years 1997 to 2020. The dataset consists of 19,689 data points, each with ten features including Crop, Season, Crop_Year, State, Annual_Rainfall, Area, Production, Pesticide, Fertilizer, and Yield. The dataset encompasses 55 different types of crops cultivated across India. The crop yield dataset was used to prediction of crop yield using regression with stacking ensemble model. The dataset is split into training 80% and testing 20%.

本研究采用印度27个邦及3个中央直辖区的作物单产历史数据，时间覆盖1997年至2020年。该数据集共包含19689条数据样本，每条样本涵盖10项特征，依次为作物（Crop）、种植季（Season）、作物年份（Crop_Year）、行政区域（State）、年降雨量（Annual_Rainfall）、种植面积（Area）、总产量（Production）、农药施用量（Pesticide）、化肥施用量（Fertilizer）以及单产（Yield）。该数据集覆盖印度境内种植的55类不同作物。本作物单产数据集被应用于基于堆叠集成回归模型的作物单产预测任务，且按80%训练集、20%测试集的比例进行划分。