Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship ( R 2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.

中文翻译：

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使用 Landsat 8 OLI 估算薄荷作物生物量产量的人工神经网络模型

产量预测对于管理一个国家的粮食和农业经济增长至关重要。基于人工神经网络 (ANN) 的模型已被广泛用于进行精确和现实的预测，特别是对于非线性和复杂的问题，如作物产量预测、生物量变化检测和作物蒸发蒸腾检测。在本研究中，各种参数即。Landsat 8 OLI（操作土地成像仪）卫星数据和派生的光谱指数以及田间库存数据的光谱带使用多层感知器的人工神经网络技术评估薄荷作物生物量估计。估计的生物量与现场测量的生物量显示出良好的关系（R 2 = 0.762 和均方根误差 (RMSE) = 2.74 t/ha）。