DOI: 10.5061/dryad.cvdncjtfm
title: Dataset for worker activity recognition and efficiency estimation during manual harvesting

description: This dataset contains harvest data collected during manual strawberry
 harvesting with instrumented picking carts in Santa Maria, CA, USA, in
 2024. The data includes geo-tagged harvest mass, cart location, and motion
 recorded by a GPS receiver, an Inertial Measurement Unit (IMU), and load
 cells. Each data point is annotated as either "Pick" (indicating
 active picking) or "NoPick" (indicating no active picking). This
 dataset can be used to train, validate, and test AI algorithms to
 recognize worker activity during manual fruit harvesting and quantify
 worker efficiency. It is valuable for researchers and practitioners in
 precision agriculture and agricultural automation who are working on
 optimizing labor and field management, as well as developing strawberry
 harvesting machines or harvest assist systems.
description: Data was collected from instrumented picking carts, iCarrtios, in
 a commercial strawberry field in Santa Maria, California, growing
 Fronteras variety. Plants were cultivated in raised beds with a width of
 110 cm, each containing four parallel strawberry rows.
 iCarritos were developed by instrumenting a wire frame structure
 picking carts (aka carritos) with a wheelbarrow system. Carts were
 equipped with a SwiftNav Piksi GNSS unit with an integrated inertial
 measurement unit (IMU) to record the geospatial position of harvest and
 cart motion. The GNSS unit had a horizontal Circular Error Probable (CEP)
 accuracy of 0.75 meters. Two load cells were installed in front and rear
 of the cart to measure harvest mass. Recorded harvest mass was obtained by
 averaging the front and rear mass readings. A Raspberry
 Pi 0W microcomputer was used as the central processing unit, and an SD
 card was used to run the carrito software and store the data during the
 harvest at 10 HZ.
description: # Dataset for worker activity recognition and efficiency estimation during
 manual harvesting Dataset DOI:
 [10.5061/dryad.cvdncjtfm](10.5061/dryad.cvdncjtfm) ## Description of the
 data and file structure The main goal of the study was to develop a
 practical and low-cost yield monitoring system for manually harvested
 strawberries to encourage widespread adoption. A key decision in the
 system design was to utilize an affordable GPS that relies on the freely
 and widely available Satellite Based Augmentation System (SBAS)
 corrections. The pickers were not trained prior to using the iCarritos and
 were deployed in a commercial field with minimal disruption to established
 harvesting practices. Data was collected as part of the USDA Agricultural
 Research Service (USDA-ARS) Areawide Pest Management Grant Program project
 "Site-Specific Soil Pest Management in Strawberry and Vegetable
 Cropping Systems Using Crop Rotation and a Needs-Based Variable Rate
 Fumigation Strategy” through Non-Assistance Cooperative Agreements. The
 publicly released dataset consists of two main folders,
 `SantaMaria_CNNLSTM` and `SantaMaria_2024`, along with corresponding Excel
 files that record harvested trays. The `SantaMaria_CNNLSTM` folder was
 used to train and evaluate the CNN–LSTM model, as well as for picker
 efficiency and tray fill time computation and evaluation. The
 `SantaMaria_2024` folder was used to compute picker efficiency and tray
 fill time over the entire harvest season. The following section provides a
 detailed description of each component. 1. **`SantaMaria_CNNLSTM.zip`:**
 The `cart_data` subfolder within this directory contains the annotated
 harvest data used for CNN–LSTM model training and evaluation. It includes
 multiple CSV files, each corresponding to a specific harvest day,
 following the naming convention `mm-dd-yy_train-ready_all_carts.csv`
 (e.g., `4-10-24_train-ready_all_carts.csv`). Each CSV file contains the
 following columns: * `date_cartID`: Combined harvest date and iCarrito
 identifier. * `GPS_TOW`: GPS Time of Week (milliseconds). * `easting`,
 `northing`: UTM coordinates (metres). * `ax`, `ay`, `az`: Acceleration
 along the x, y, and z axes (m s⁻²). * `raw_mass`: Raw harvest mass (kg). *
 `activity`: Annotated activity label, either “Pick” or “NoPick”. The
 `break_log` subfolder within this directory contains daily break
 information for each iCarrito. Files follow the naming format
 `mm-dd-yy_break_log.csv` and include: * `harvest_date`: Harvest date. *
 `cart_id`: iCarrito identifier. * `no_breaks`: Number of breaks taken by
 the corresponding iCarrito. The accompanying Excel file,
 `harvested_trays.xlsx`, records the number of trays harvested per iCarrito
 for each harvest day, with following columns: * `cart_id`: iCarrito
 identifier. * `no_trays`: Number of trays harvested by that iCarrito. 2.
 **`SantaMaria_2024.zip`:** This folder contains the complete season-long
 harvest data from all iCarritos, including break logs and harvested tray
 records. The subfolders are organised by date (e.g., `DD-MM-YY/`) and
 contain raw harvest (`cart1.csv`, `cart2.csv`, …, `cartN.csv`)
 corresponding to individual carts. Each CSV file contains ten columns: *
 `rpi_utc_time`: Raspberry Pi UTC timestamp. * `gps_utc_time`: GPS UTC
 timestamp. * `GPS_TOW`: GPS Time of Week (milliseconds). * `LAT`, `LON`,
 `HEIGHT`: Geographic coordinates (degrees and metres). * `ax`, `ay`, `az`:
 Acceleration along the x, y, and z axes (m s⁻²). * `raw_mass`: Raw harvest
 mass (kg). The `break_log` subfolder and the Excel file
 `harvested_trays_seasonlong.xlsx` follow the same structure and naming
 conventions as described above, containing daily break records and total
 harvested trays for all carts across the season. The dataset provides
 comprehensive, annotated time-series data suitable for training and
 evaluating activity recognition models, as well as for further research in
 harvest process optimisation and labour efficiency analysis. ## Dataset
 directory structure ``` datasets/ ├── SantaMaria_CNNLSTM/ │ ├── cart_data/
 │ │ ├── MM-DD-YY_train-ready_all_carts.csv │ │ ├── ... │ │ └──
 MM-DD-YY_train-ready_all_carts.csv │ ├── break_log/ │ │ ├──
 MM-DD-YY_break_log.csv │ │ ├── ... │ │ └── MM-DD-YY_break_log.csv │ └──
 harvested_trays.xlsx │ ├── SantaMaria_2024/ │ ├── DD-MM-YY/ │ │ ├──
 cart1.csv │ │ ├── cart2.csv │ │ ├── ... │ │ └── cartN.csv │ ├── break_log/
 │ │ ├── MM-DD-YY_break_log.csv │ │ ├── ... │ │ └── MM-DD-YY_break_log.csv
 │ └── harvested_trays_seasonlong.xlsx ``` ## Code/software
 [https://github.com/uddhavbhattarai/HumanActivityRecognitionEfficiencyEstimation](https://github.com/uddhavbhattarai/HumanActivityRecognitionEfficiencyEstimation) ## Human subjects data While this dataset is derived from field operations involving human workers, it does not contain any personally identifiable information (PII). The dataset consists solely of sensor data collected from instrumented strawberry-picking carts (iCarritos), including GPS coordinates, timestamps, IMU measurements, and load-cell readings. No personal attributes, names, worker identifiers, or demographic information were collected. Carts were used interchangeably by multiple pickers during commercial harvesting operations, and no individual was permanently or uniquely associated with any specific cart. As a result, there is no linkage between the recorded data and any identifiable person.