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| Aspect | Description | |--------|-------------| | | Develop a scalable pipeline that predicts end‑of‑season grain yield with < 15 % mean absolute percentage error (MAPE) across diverse agro‑ecological zones. | | Data | - Remote sensing: Sentinel‑2 multispectral imagery (10 m resolution) every 5 days. - In‑field IoT sensors: Soil moisture, temperature, and nutrient probes (1 Hz sampling). - Historical agronomic records: Variety, planting date, management practices (≈ 30 yr). | | Study sites | 12 research farms spanning three climate clusters (Mediterranean, temperate, semi‑arid) in Europe and North America, covering 5 000 ha in total. | | Model | A hierarchical deep‑learning architecture : 1. Low‑level encoder (CNN) processes satellite patches. 2. Temporal module (GRU) ingests IoT time series. 3. Meta‑learner (gradient‑boosted trees) merges encoder outputs with categorical agronomic metadata. | | Training & validation | 5‑fold cross‑validation across sites, with a hold‑out year (2020) for out‑of‑sample testing. | | Key performance metrics | - MAPE: 12.8 % (vs. 15.9 % for the baseline “YieldNet”). - R²: 0.78 (vs. 0.71). - Computation time: 3 h per season on a single NVIDIA V100 GPU (≈ 30 % faster than baseline). | | Open‑source deliverables | - MEYD‑Toolkit (Python package, pip‑installable). - Docker‑based cloud‑ready pipeline (AWS, GCP). - Public dataset (2 TB) hosted on Zenodo (doi:10.5281/zenodo.1234567). | - In‑field IoT sensors: Soil moisture, temperature, and
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MEYD873 was created in response to growing interest in using high-resolution, multi-source data to evaluate microtrip patterns, curb use, and first/last-mile behavior in densely populated cities. Compiled by a consortium of academic researchers, local transport agencies, and a private mobility analytics firm, the dataset combined anonymized GPS traces, shared-micromobility trip logs (e-scooters and bikes), transit smartcard tap records, and curbside sensor counts collected during 2019–2020 and released publicly in 2021.
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