Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters

Nandini Babbar, Ashish Kumar, Vivek Kuma Verma
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Abstract

Early-season Crop Yield Prediction can assist farmers in India's leading economic sector of agriculture by assisting them in formulating their decision-making strategies. Deep Learning approaches have surpassed conventional statistical methods for yield prediction and crop forecasting as the artificial intelligence field has grown. The goal of the current work is to employ a LSTM model to estimate wheat crop yields in India. The dataset in this paper consist of soil and the metrological parameters. On the basis of consideration of individual factor one at a time, soil parameters such as temperature, humidity, moisture, soil type, crop, nitrogen, potassium, phosphorous in addition to nourishment used with consideration of metrological data, it contains minimum and maximum temperature as well as rainfall. At the end, we are able to get the accuracy and mean absolute error with R2 value for both the parameters. Later, we can merge these two parameters and get more efficient results for accurate prediction.
考虑土壤和计量参数的长短期记忆预测小麦产量
早季作物产量预测可以帮助印度农业主要经济部门的农民制定决策策略。随着人工智能领域的发展,深度学习方法在产量预测和作物预测方面已经超越了传统的统计方法。当前工作的目标是使用LSTM模型来估计印度的小麦作物产量。本文的数据集由土壤和计量参数组成。在一次考虑单个因素的基础上,土壤参数如温度、湿度、水分、土壤类型、作物、氮、钾、磷等,除考虑气象数据使用的营养外,还包括最低和最高温度以及降雨量。最后,我们可以得到两个参数的精度和R2值的平均绝对误差。稍后,我们可以合并这两个参数,得到更有效的结果,以进行准确的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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