利用遥感数据进行农业商品到货预测：洞察及展望
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond
摘要：
在像印度这样的发展中国家，农业在人口生活中发挥着极其重要的作用。在印度，大约80%的人口依赖农业或其副产品作为主要就业手段。鉴于大量人口依赖农业，政府提前估计市场因素并为任何偏离这些估计的情况做好准备变得极为重要。商品到达市场是一个极为重要的因素，在全国各地的地区一级都有体现。政府利用历史数据和对商品到货、价格、作物质量等重要变量的短期预测，采取积极步骤，决定各种政策措施。在本文中，我们提出了一个框架，结合遥感数据预测未来的商品到货短时间序列。我们处理的是非常高维的数据，这些数据超过了观测大小的多个数量级。我们使用级联层的降维技术结合正则化回归模型进行预测。我们目前的结果，以预测抵达主要市场和国家的价格为'图尔'（红克）作物在卡纳塔克邦，印度。我们的模型在许多情况下始终击败流行的ML技术。我们的模型是可扩展的，时间效率高，可以推广到许多其他作物和地区。我们从回归参数中获得了多方面的见解，其中一些是预测未来价格等更复杂数量时需要考虑的重要方面。我们还将这些见解结合起来，为不同的政府组织提供重要的建议。
英文摘要：
In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the government to estimate market factors in advance and prepare for any deviation from those estimates. Commodity arrivals to market is an extremely important factor which is captured at district level throughout the country. Historical data and short-term prediction of important variables such as arrivals, prices, crop quality etc. for commodities are used by the government to take proactive steps and decide various policy measures. In this paper, we present a framework to work with short timeseries in conjunction with remote sensing data to predict future commodity arrivals. We deal with extremely high dimensional data which exceed the observation sizes by multiple orders of magnitude. We use cascaded layers of dimensionality reduction techniques combined with regularized regression models for prediction. We present results to predict arrivals to major markets and state wide prices for `Tur' (red gram) crop in Karnataka, India. Our model consistently beats popular ML techniques on many instances. Our model is scalable, time efficient and can be generalized to many other crops and regions. We draw multiple insights from the regression parameters, some of which are important aspects to consider when predicting more complex quantities such as prices in the future. We also combine the insights to generate important recommendations for different government organizations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
