DOI: 10.5061/dryad.v6wwpzh7h
title: Dataset for precision yield estimation and mapping in manual strawberry harvesting

description: This dataset contains harvest data collected during manual strawberry
 harvesting using instrumented picking carts (iCarritos) in two California
 fields over a harvest season. The data includes geo-tagged harvest mass
 and cart motion information recorded by load cells, a GPS receiver, and
 Inertial Measurement Units (IMU). The dataset enables data-driven yield
 estimation and mapping for manually harvested strawberries. Furthermore,
 the dataset could be used to optimize robotic strawberry harvest machines
 or harvest assist systems, such as crop transportation systems. This
 dataset is valuable for researchers and practitioners in precision
 agriculture, offering a foundation for developing scalable automation and
 precision agriculture technology in specialty crop harvesting. 
description: Data was collected from iCarritos developed by modifying a
 conventional wireframe structure with a wheelbarrow system, integrated
 with sensors, mounting systems, and control hardware. Two load cells were
 installed at the front and back of each iCarrito, and the harvest mass was
 obtained by averaging the readings from these load cells. A significant
 design choice was to ensure that the iCarritos were low-cost and
 practical. To achieve this, the carts utilized an affordable Piksi Multi
 GNSS unit (Swift Navigation Inc., USA) with an integrated IMU. The GNSS
 unit operated based on SBAS localization and has a rated horizontal
 Circular Error Probable (CEP) accuracy of 0.75 meters. This unit was used
 to measure location (latitude and longitude) as well as acceleration data
 along the x, y, and z axes (ax, ay, az).  A Raspberry
 Pi Zero W (Raspberry Pi Foundation, UK) microcomputer served as the
 central processing unit, with an SD card used to run the cart software and
 store data during harvest operations. The data was stored at the rate of
 10 Hz. The control system consisted one main power toggle switch connected
 directly to the battery, a control switch for selecting operation modes,
 and two status LEDs for system feedback.  Before taking
 the iCarritos into the field, each cart's load cells were calibrated.
 Following each harvest, the collected data was reviewed and the load cells
 were recalibrated if necessary. Commercial strawberry pickers, who were
 experienced in manual harvesting but not specifically trained on the
 iCarritos, utilized these instrumented carts during normal harvesting
 operations. The pickers were compensated for their time using the
 iCarritos in the trial field. They were instructed to use the iCarritos as
 they would their regular carts, with minimal changes to their normal
 harvesting routine.  Studies were conducted in two
 major strawberry-producing regions in California during the 2024
 strawberry growing season. The selected fields were commercial strawberry
 fields in Santa Maria, growing the Fronteras variety, and in Salinas, CA,
 growing the Cabrillo variety. The width of the raised beds in Santa Maria
 were 110 cm wide while those in Salinas were 75 cm wide. The raised bed
 centers were also pre-mapped using RTK-GNSS, providing centimeter-level
 accuracy under field conditions.
description: # Dataset for precision yield estimation and mapping in manual strawberry
 harvesting with instrumented picking carts Dataset DOI:
 [10.5061/dryad.v6wwpzh7h](10.5061/dryad.v6wwpzh7h) ## Description of the
 data and file structure ## Background The main goal of the study was to
 develop a practical, low-cost yield monitoring system for manually
 harvested strawberries to encourage widespread adoption. A key design
 decision was to use an affordable GPS relying on the freely available
 Satellite Based Augmentation System (SBAS) corrections. Pickers were not
 trained prior to using the iCarritos, and the system was deployed in a
 commercial field with minimal disruption to established harvesting
 practices. Data were 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. ## Value of the
 Data * This is a comprehensive dataset for developing and validating
 precision yield estimation and mapping techniques for manually harvested
 crops, especially strawberries.  - The dataset enables the data-driven
 analysis of spatial and temporal yield variability in strawberry fields,
 which can inform decision-making in field management, including nutrient
 application, irrigation, and pest control.  * Researchers in precision
 agriculture can reuse this data to develop new algorithms for worker
 activity recognition, efficiency estimation, yield forecasting, and crop
 management strategies.  - Agricultural engineers and data scientists can
 utilize this dataset to improve existing data processing pipelines or
 develop new ones for handling sensor data from manual harvesting
 operations.  - The data is gathered from scalable, practical, and low-cost
 picking carts, which could provide practical solutions for yield
 estimation and mapping for manually harvested crops, potentially
 benefiting both researchers and industry practitioners.  ## Files and
 variables ### File: datasets.zip **Description:** The publicly released
 dataset consists of three main folders: `BedCenters`, `SantaMaria`, and
 `Salinas`. The `BedCenters` folder contains two files, `Salinas2024.txt`
 and `SantaMaria2024.txt`, each providing the coordinates of the bed center
 endpoints. The `SantaMaria` and `Salinas` folders contain harvest data
 from all carts collected throughout the harvest season. The following
 section provides a detailed description of each component. 1.
 **`SantaMaria, Salinas`** **folders:** These folders contain complete
 season-long harvest data from all iCarritos, along with harvested tray
 records. The subfolders are organised by date (e.g., `DD-MM-YY/`) and
 contain raw harvest files (`cart1.csv`, `cart2.csv`, …, `cartN.csv`)
 corresponding to individual carts. Each CSV file contains following
 columns: * `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 accompanying Excel file, `harvested_trays.xlsx`, records
 the number of trays harvested per iCarrito for each harvest day, with the
 following columns: * `cart_id`: iCarrito identifier. * `no_trays`: Number
 of trays harvested by that iCarrito. ## Dataset directory structure ```
 datasets/ ├── BedCenters/ │ ├── Salinas2024.txt │ └── SantaMaria2024.txt
 ├── SantaMaria/ │ ├── DD-MM-YY/ │ │ ├── cart1.csv │ │ ├── cart2.csv │ │
 ├── ... │ │ └── cartN.csv │ └── harvested_trays.xlsx └── Salinas/ ├──
 DD-MM-YY/ │ ├── cart1.csv │ ├── cart2.csv │ ├── ... │ └── cartN.csv └──
 harvested_trays.xlsx ``` ## Code/software
 [https://github.com/uddhavbhattarai/iCarritoYieldEstimationandMapping.git](https://github.com/uddhavbhattarai/iCarritoYieldEstimationandMapping.git) ## Human subjects data While this dataset was 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.