Precision coffee growing is a concept that implies the use of precision agriculture techniques in coffee plantations. For the coffee growing, the precision electronic resources coupled to the harvesters are very scarce. Thereby, the harvest of coffee plantations that compose the grid sampling for generation of thematic maps can be performed manually. The aim of the present study was to generate a linear regression model to estimate the time required to harvest, estimate the labor costs to harvest manually the georeferenced sample points for generation of coffee yield maps. The study was performed in a coffee area of 56 hectares using two sampling points per hectare, totaling 112 points, being evaluated four coffee plants for each point. The manual harvest of the points was performed by four rural workers with experience in the coffee harvest. Afterwards, the collected volume was measured by a graduated container and the times were obtained by the digital stopwatch. Based on the data obtained in the field, a linear correlation model was established between the harvest time of each sampling point and the yield of the point, whose R² value was 78.27, cost was R$ 8.92 per point. These results are relevant for estimating the amount of labor force required to generate manually harvest yield maps according to the producer’s coffee yield estimate, contributing to the closure of the precision coffee growing cycle. Key words: Precision agriculture; crop mapping; field efficiency; costs.

The performance of grain storage silos is strongly influenced by discharge flow patterns, hopper geometry, and material properties such as moisture content and impurity levels. However, the combined effects of these factors on flow behavior, discharge rate, and segregation are not yet fully understood. This study experimentally investigated the integrated effects of moisture content, prismatic hopper geometry (hopper angle β), and impurity addition on flow behavior, segregation, and mass flow rate in reduced-scale silos. Experiments were conducted using three prismatic silos with hopper angles of β = 15°, 33°, and 45°, filled with maize at moisture contents of 13.6%, 20.2%, and 26.0% (wet basis), under both clean conditions and with the addition of 10% impurities (fraction passing through a 5 mm sieve). The discharge rate was determined by direct mass–time measurements, flow patterns were inferred from video analysis, and segregation was quantified based on the mass fraction of impurities in samples collected during discharge. The results indicate that moisture content was the most influential factor, reducing the discharge rate by up to 22.8% when increasing from 20.2% to 26.0% w.b. (p < 0.05). Hopper geometry also had a significant effect, with performance differences among configurations becoming more pronounced under high-moisture conditions. The addition of 10% impurities increased the discharge rate under all tested conditions, with gains of up to 29.0% at 26.0% w.b. and β = 15°. Segregation intensified with increasing moisture content, leading to a progressive accumulation of impurities toward the end of discharge. The stick–slip phenomenon was observed under a critical condition (26.0% w.b., β = 15°, with impurities), resulting in a 23.0% reduction in the average discharge rate compared to the equivalent stable condition. These findings demonstrate that granular flow behavior in silos is governed by the interaction between moisture, hopper geometry, and material composition. The results also suggest that operational strategies such as pre-cleaning should be evaluated in conjunction with expected moisture conditions, as pre-cleaning may adversely affect flow performance under high-moisture scenarios.