Development and evaluation of a novel system for monitoring harvest labor efficiency

This paper introduces a real-time labor monitoring system (LMS) with the ability to track and record individual picker efficiency during manual harvest of specialty crops. This system utilizes existing commercial harvest equipment and integrates a digital weighing scale, RFID reader, computational unit, and a portable datalogger carried by pickers. The RFID reader, digital scale and computational unit are assembled on a common portable chassis. As pickers transfer fruit into a standard collection bin, the system reads the picker’s ID (RFID tag) and the weight of fruit. Weight data can then be transmitted wirelessly to the picker’s datalogger which records and displays the incremental and total weight of harvested fruit. An algorithm was developed in Matlab® to record, process and store the data, as well as to transmit wirelessly the weight value to the wearable datalogger. System prototypes were assembled and field-tested for accuracy and reliability during commercial harvest of sweet cherries (Prunus avium L.) in the Pacific Northwest. The LMS reliably calculated the harvest rate, picking cost, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket, and mean fruit weight per bucket, in real time. The mean harvest rate (±standard error) in a ‘Chelan’/Mazzard sweet cherry orchard trained to a steep leader architecture was 0.53 ± 0.13 kg/person/min. Harvest rate was similar for the same genotype trained to a steep leader (3 leaders) at 0.50 ± 0.10 kg/person/min and for harvest of ‘Tieton’/‘Gisela®6’ trees trained to a central leader architecture (0.53 ± 0.15 kg/person/min). When surveyed after using the LMS, every picker indicated a preference for knowing the exact weight of fruit they harvested compared with the current system of reimbursement per bucket or bin. Using the LMS, reliable data on worker efficiency can be collected with minimal interference with standard commercial practices.