SharpmetriX Dataset: IoT-Based Harvest Monitoring for Yield and Labor Productivity Mapping in Viticulture

"SharpmetriX: A Cost-Effective IoT Solution for Mapping Harvest Vineyards Yields and Labor Productivity Indicators", introducing the SharpmetriX system for in-harvest monitoring in viticulture. The dataset was collected during field experiments conducted in September 2024 at the Centro de Estudos Vitivinícolas do Dão (CEVDAO), Nelas, Portugal, using custom-developed IoT add-ons attached to harvesting tools (secateurs and collection containers), combined with a smartphone-based data acquisition system. The dataset comprises three complementary experimental scenarios, reflecting both controlled validation conditions and real-world harvesting operations. The dataset is organized into three main components, each corresponding to a distinct experimental scenario. 1. Parallel Harvest Dataset File: Device_cuts_CEVDAO_PH.csv This dataset corresponds to a real harvesting operation where four workers harvested vine rows simultaneously, each equipped with instrumented secateurs. Contents Timestamped cut events Geographic coordinates (latitude, longitude) Device identifier (corresponding to each harvester/tool) Event-related information associated with each cut Characteristics Data reflect real-world variability and operator behavior Suitable for: Spatial attribution (e.g., row assignment algorithms) Multi-operator analysis Evaluation of unsupervised methods 2. Controlled Harvest Datasets Files: Vine_3.csv Vine_15.csv These datasets correspond to controlled harvesting experiments, where a single operator harvested individual vines under monitored conditions, allowing comparison with ground truth. Contents Timestamped data stream including: Cut events Accumulated harvested weight (grams) Acceleration measurements (milli-g) Geographic coordinates Data Encoding The column “grams & cuts” contains mixed information: The value "CUT" indicates a cutting event Numeric values correspond to accumulated harvested weight 3. RTK Benchmark Dataset File: RTK_data.csv This dataset was collected to evaluate positioning accuracy, comparing smartphone-based GNSS with RTK-corrected positioning. Contents Latitude and longitude: With RTK correction Without RTK correction Cut state: cut = 1 → secateurs closed (cut event) cut = 0 → secateurs open Enables analysis of: GNSS positioning error Effect of operator movement and posture Spatial accuracy improvements with RTK