This experiment investigated the relationship between tobacco canopy spectral characteristics and tobacco biomass. A completely randomized design, with plantings on the 15th of September, October, November, and December, each with 9 variety × fertiliser management treatments, was used. Starting from 6 weeks after planting, reflectance measurements were taken from one row, using a multispectral radiometer. Individual plants from the other 3 rows were also measured, and the above ground whole plants were harvested and dried for reflectance/dry mass regression analysis. The central row was harvested, cured, and weighed. Both the maximum NDVI and mass at untying declined with later planting and so was the mass-NDVI coefficient of determination. The best fitting curves for the yield-NDVI correlations were quadratic. September reflectance values from the October crop reflectance were statistically similar ( ), while those for the November and the December crops were significantly different ( ) from the former two. Mass at untying and NDVI showed a quadratic relationship in all the three tested varieties. The optimum stage for collecting spectral data for tobacco yield estimation was the 8–12 weeks after planting. The results could be useful in accurate monitoring of crop development patterns for yield forecasting purposes. 1. Background Crop yield estimation in many countries is based on conventional techniques of data collection and ground-based field reports [1]. A variety of mathematical models relating to crop yield have also been proposed in recent years for many crops [2, 3]. In Zimbabwe crop surveys are mostly used in estimating crop yield [4]. The method is costly, time consuming, and is prone to large errors due to incomplete ground observations, leading to poor crop yield assessment and crop area estimations [1]. Remote sensing data has the potential and the capacity to provide spatial information in a global scale of features and phenomena on earth on an almost real-time basis [1]. Use of remote sensing techniques has the potential to provide quantitative and timely information on agricultural crops over large areas, and many different methods have been developed to estimate crop yields [5–7]. In general, the use of remote sensing was aimed at reducing the number of samples of ground surveys, making it less expensive [8]. With the application of remote sensing in agriculture, there is potential not only in identifying crop classes but also in estimating crop yield [1]. Spectral measurements from crops can be used in estimating crop parameters such
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