Predicting in-season crop yield is a unique tool for drawing important crop management decisions for precision farming. Field experiments were conducted at two locations in northwestern India under different agro-climatic zones to predict and validate spring maize yield using various in-season spectral indices. The spectral properties measured with leaf color chart (LCC), chlorophyll meter (SPAD meter), and GreenSeeker optical sensor were used to predict grain yield. A power function based on the Normalized Difference Vegetative Index (NDVI) measured with GreenSeeker optical sensor at V9 growth stage (9th leaf with fully exposed collar) presented higher values of coefficient of determination and explained 61% of the variability in spring maize grain yield, whereas NDVI measured at early and late growth stages were not reliable for the purpose. The spectral properties recorded with the SPAD meter and LCC rendered better grain yield estimates at VT growth stage (tasseling) and were respectively able to explain 75 and 76% variability in grain yield. The developed models were validated on an independent data set from another field experiment on spring maize. The normalized root mean square error (NRMSE) was <10% for LCC and SPAD at all the growth stages and at V9 growth stage for NDVI. The LCC, SPAD, and NDVI values adjusted with cumulative growing degree day were not helpful to improve NRMSE.

预测当季农作物的产量是为精确农业制定重要农作物管理决策的独特工具。在印度西北部两个不同农业气候区的两个地点进行了田间试验，以使用各种季节光谱指数预测和验证春玉米的产量。使用叶色图（LCC），叶绿素仪（SPAD仪）和GreenSeeker光学传感器测得的光谱特性可用于预测谷物产量。基于GreenSeeker光学传感器在V9生长阶段（第9个叶片完全暴露的衣领）测量的归一化植物生长指数（NDVI）的幂函数表示出更高的测定系数值，并解释了61％的春玉米籽粒产量变异性而在生长早期和晚期测量的NDVI并不可靠。用SPAD计和LCC记录的光谱特性在VT生育阶段（抽穗处理）提供了更好的谷物产量估计，并且分别能够解释谷物产量的75％和76％的变异性。在另一个来自春玉米田间试验的独立数据集上验证了开发的模型。对于LCC和SPAD，在所有生长阶段以及在NDVI的V9生长阶段，归一化均方根误差（NRMSE）均小于10％。用累积生长度日调整的LCC，SPAD和NDVI值无助于改善NRMSE。在另一个来自春玉米田间试验的独立数据集上验证了开发的模型。对于LCC和SPAD，在所有生长阶段以及在NDVI的V9生长阶段，归一化均方根误差（NRMSE）均小于10％。用累积生长度日调整的LCC，SPAD和NDVI值无助于改善NRMSE。在另一个来自春玉米田间试验的独立数据集上验证了开发的模型。对于LCC和SPAD，在所有生长阶段以及在NDVI的V9生长阶段，归一化均方根误差（NRMSE）均小于10％。用累积生长度日调整的LCC，SPAD和NDVI值无助于改善NRMSE。