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Yield mapping technologies can help to increase the quantity and quality of agricultural production. Current systems only focus on the quantification of the harvest, but the quality has equal or greater importance in some perennial crops and impacts directly on the financial profitability. Therefore, a system was developed to quantify and relate the quality obtained in the classification line with the plants of the orchard and for decision-making. The system is comprised of hardware, which obtains the location of the harvester bag during harvesting and unloading at the unloading site, and software that processes the collected data. The cloud of real-time data contributed from the different collectors (bins) allows the construction of yield maps, considering the multi-stage harvesting system. Further, the system enables the creation of a detailed map of the plants and fruits harvested. As the harvest focuses on quality, it takes place in stages, depending on the ripening of the fruits. In addition to the yield maps, the system allows identification of the efficiency of each worker undertaking the harvest by the number of performed discharges and by the time spent. The system was developed in partnership with the Federal Technological University of Paraná and Embrapa Uva & Vinho and was tested in apple orchards in southern Brazil. Although the system was evaluated with only data from apple cultivation, monitoring the quality and quantifying other orchard fruits can positively impact the fruit sector. [ABSTRACT FROM AUTHOR]

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framework for software that provides yield data cleaning and yield opportunity index ( Y) calculation for site-specific crop management (SSCM). The artifacts in many yield data sets, which inevitably occur, can pose a significant effect on the validity of Yi. Automated and standardised yield correction procedures were designed to improve the data quality by removing: (1) unreasonable outliers; (2) distribution outliers (globally and locally); and (3) position errors. The calculation of Yi uses two aspects of crop yield assessment, the magnitude of yield variation and the spatial structure of the variation. The cleaning algorithms were applied to four yield data sets with known integrity issues to demonstrate effectiveness. Approximately 13-20 % of the original yield data were removed, and this resulted in an increased mean yield of 0.13 t/ha (average). The semivariograms of cleaned data were shown to possess smaller nugget values compared with the original data. The opportunity index calculation algorithm was demonstrated on a field with nine seasons of yield data. The results demonstrated that using a ranking of Yi provides a rational, agronomic assessment of the opportunity for SSCM based on the quantity and pattern of production variability displayed in yield data sets. This provides farm managers with a rapid way to assess whether the observed variability deserves further investigation and eventual investment in SSCM operations. [ABSTRACT FROM AUTHOR]

Many yield maps exhibit systematic errors that attenuate the underlying yield variation. Two errors are dealt with in detail in this paper: those that occur when the harvester has a narrow finish to a land and those that occur when the harvester is filling up at the start of a harvest run. The authors propose methods to correct or remove erroneous data by the use of an expert filter, or alternatively use of an interpolation technique called potential mapping. [ABSTRACT FROM AUTHOR]