Academic literature on the topic 'Cotton – Precision planting'

Abstract.Cotton ( L.) producers were some of the earliest adopters of practices employing site-specific management. Yield monitors are an essential component of those precision agriculture methods. The agreement between measured cotton yields and those estimated with yield monitors must be improved to adequately advance precision agriculture, particularly the site-specific planting of multiple varieties. Six site-years of observations with two yield monitor systems were studied with the objective to compare yield monitor-estimated weights to observed weights in replicated cotton variety trials and investigate factors that could relate to any observed varietal effect. The results supported the findings of earlier research indicating that cotton yield monitor calibration is affected by variety. For the six site-years and three combinations of two site-years, correlations were sometimes observed between calibration errors and turnout or some of the High Volume Instrument (HVI) fiber quality measurements, but no factor was consistently correlated across multiple studies. In one study, none of the included factors was significantly correlated with calibration error, while the HVI properties reflectance (Rd) and leaf grade were both significantly correlated in three comparisons, which was more than any other factor. Data from additional site-years must be collected and analyzed and other factors investigated to find what variety-related properties affect yield monitor errors and to allow the determination of calibration adjustments based on those factors. Keywords: Cotton, Cotton picker, Harvest, Precision agriculture, Yield monitor.

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.