UC Davis

Estimating and recording a worker's picking rate during tree fruit harvesting can provide useful information for better workforce management, orchard platform crew management, and generation of yield maps (in combination with position). A commercial picking bag was instrumented to estimate harvested fruit weight in real-time. All electronics were placed inside an enclosure that was placed between the bag and its shoulder straps, without hindering picking motions. The electronics included two load cells to measure the forces exerted on the straps by the bag and fruits, an Arduino microcontroller, signal conditioning circuits, data storage, wireless communication components, and inertial sensors. Software was developed for data acquisition, filtering, transmission, and storage. Two calibration models were developed to estimate fruit weight. One model (model 2) used inertial sensor data to compensate for the picking bag's angle with respect to gravity direction, while the other model (model 1) did not. Dynamic calibration experiments were performed over the entire weight range of the bag (0 to 20 kg) with reference objects of known weight (baseballs and fresh apples). The weight was divided into three ranges: light load (<8 kg), medium load (8 to 13 kg), and heavy load (>13 kg). Results showed that model 1 performed slightly better in the light load range, but model 2 was superior in the medium and heavy load ranges, presumably due to bag angle compensation. The best root mean squared error over the entire range was achieved by model 2 and was 0.36 kg (1.8% of bag capacity). In an application case study, two bags were used by workers harvesting from a platform in a commercial apple orchard. From the data, the pickers' harvesting speeds were estimated, and the fruit yield distribution was calculated for one side of a tree row.