ArticleBMC plant biology2026
Enhancing labour efficiency in coffee harvesting using process mining and statistical analysis.
Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoffee processing, in wet processing environments, where operational challenges are prevalent, is a pivotal aspect in product quality and market value. The harvesting and processing of coffee in Sakaleshpur (Arabica) and Chikkamagaluru (Robusta) are examined in this study. It focuses on 15 batches of Robusta harvesting and three different stages of Arabica harvesting: fly picking, mid harvesting, and stripping. The analysis of process features and productivity-impacting factors is conducted with the help of statistical analysis and Process Mining (PM).
findingsThe results indicate that there is a strong negative correlation between the number of workers and the quantity of coffee picked in the process of fly picking, that is, the increased number of labour does not necessarily lead to increased output. Statistical analysis has shown that labour efficiency, weather and gender are key factors affecting the harvesting performance. Although cause-and-effect research can determine significant factors influencing operational efficiency, PM can identify variations in workflow patterns.
conclusionsThe paper demonstrates that integrating PM and statistical techniques can provide informative data on the way coffee harvesting takes place. The results highlight how important it is to manage labour effectively and take human and environmental aspects into account in order to increase productivity. This method provides a data-driven paradigm for increasing coffee processing systems' efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.