Water scarcity represents one of the most critical threats to global food security. Citrus and almond cultivation in semi-arid Mediterranean climates accounts for over 4.2 million hectares worldwide, yet conventional irrigation management wastes an estimated 30–40% of applied water through temporal and spatial mismatch. This project introduces AgroMind, a federated deep learning framework that integrates multimodal data streams — Sentinel-2 satellite imagery, distributed IoT soil-moisture sensors, and regional climate forecasts — to generate real-time, farm-scale irrigation recommendations with unprecedented accuracy and generalizability.
AgroMind advances knowledge at the intersection of federated machine learning, precision agriculture, and climate adaptation science. The central hypothesis — that privacy-preserving federated learning across heterogeneous farm networks yields superior, generalizable irrigation models compared to siloed local approaches — has not been rigorously tested at scale. This work will (1) pioneer a novel multimodal fusion architecture that synchronizes asynchronous satellite and in-situ time series; (2) establish formal theoretical bounds on federated convergence under non-IID agricultural data distributions; and (3) produce a publicly available, richly labeled dataset of 10,000+ farm-years across three climate zones, enabling a decade of downstream research.
The societal implications of AgroMind extend from individual farm economics to regional water policy. A demonstrated 25% reduction in irrigation water use across 50 pilot farms would conserve an estimated 2.1 million cubic meters annually — equivalent to the annual water supply of 14,000 households. The project will train four doctoral students (two from underrepresented groups) through an explicitly designed co-mentorship structure. Educational materials will be integrated into UPV's Agronomy Engineering curriculum, reaching 400+ students annually. All models, code, and datasets will be released under open licenses, ensuring global reproducibility and adoption.
The immediate objective of this proposal is to design, validate, and openly disseminate AgroMind — a federated deep learning system for real-time irrigation scheduling — across 50 citrus and almond farms in the Valencia region of Spain. We will test the central hypothesis that multimodal federated learning across privacy-preserving farm nodes will achieve irrigation recommendations with ≥85% accuracy and yield ≥25% water savings versus conventional scheduling, even under climate-change-accelerated precipitation variability.
Significance. Agriculture accounts for 70% of global freshwater withdrawals (FAO, 2023). In Spain alone, the agricultural sector consumes 80% of total water abstracted, yet up to 40% is wasted through temporal mismatches between irrigation application and actual crop demand (Ministerio de Agricultura, 2024). Climate change is intensifying this challenge: precipitation variability in the Mediterranean basin has increased by 23% over the past three decades and is projected to intensify under all CMIP6 scenarios. Critically, no existing decision support system addresses the privacy–generalizability tradeoff: farmers will not share raw field data, yet models trained on single-farm data do not generalize. AgroMind directly resolves this barrier.
Gaps in current knowledge. Existing precision irrigation tools (Cropland Data Layer, AquaCrop, NetIrrigation) rely on centralized data or coarse-resolution crop models. None leverage the combinatorial signal available from simultaneous fusion of high-resolution satellite phenology, real-time soil moisture dynamics, and probabilistic weather forecasting. Federated learning has been demonstrated in medical imaging and mobile keyboards; its application to heterogeneous, asynchronous agricultural IoT networks with privacy constraints remains an open, understudied problem.
Intellectual novelty across three dimensions:
The proposed research builds on a 2024–2025 pilot conducted across 12 citrus farms in L'Horta Sud (Valencia), demonstrating proof-of-concept for the core federated architecture:
Publications establishing team competence: Vidal-Roca et al. (2025) "Multimodal Deep Learning for Water Stress Detection in Citrus" — Computers and Electronics in Agriculture, 214:107821; Fuentes & Vidal-Roca (2024) "Soil Sensor Network Deployment for Precision Irrigation in Semiarid Conditions" — Agricultural Water Management, 301:108342; Sharma et al. (2025) "Federated Learning for Smart Farming: Opportunities and Challenges" — IEEE IoT Journal, 12(4):3801.
Institutional resources confirmed: UPV HPC cluster (128 NVIDIA A100 GPUs, 2 PB storage); agreements with Conselleria d'Agricultura de la Comunitat Valenciana for farm access; Purdue ACRE field station for California validation; 48 IoT sensor nodes already deployed and operational.
Overall design. A three-year mixed-methods study combining computational model development (Years 1–3), randomized field trials (Years 1–3), and dataset curation/release (Years 2–3). A pre-registration of hypotheses and analysis plans will be filed on OSF.io prior to data collection.
Year 1 — Architecture & Deployment (Jan–Dec 2027). PI Vidal-Roca will lead development of the AgroMind federated learning backbone. Key technical milestones: (i) multimodal encoder pre-training on 5 years of historical Sentinel-2 imagery for Valencia region using masked image modeling (MAE-style, ViT-B/16 backbone); (ii) IoT-to-cloud ingestion pipeline (MQTT over LoRaWAN → Apache Kafka → Delta Lake); (iii) federated aggregation server (FedProx baseline + novel topology-weighted FedAgrо variant); (iv) IoT sensor expansion from 48 to 200 nodes across 50 farms. Co-IP Fuentes will oversee field sensor deployment; Dra. Sharma will coordinate Purdue satellite data pipeline.
Year 2 — First Field Season Validation (Jan–Dec 2028). Randomized crossover trial: 50 farms divided into 25 AgroMind-managed and 25 farmer-managed (control), with crossover at Season 2. Primary endpoint: irrigation water volume (m³/ha). Secondary: yield (kg/ha citrus; kg/ha almond), WUE, energy cost per ha. Statistical power: 80% at α=0.05 to detect 20% water reduction (25 pairs; based on pilot variance σ=4.2%). IRB-equivalent animal/environment clearance: N/A (no human subjects, no vertebrates); AEMPS environmental review completed.
Year 3 — Climate Stress-Test & Open Release (Jan–Dec 2029). Apply CMIP6 downscaled climate scenarios (SSP2-4.5, SSP5-8.5) using CORDEX-EUR-11 regional model outputs to simulate 2030–2040 precipitation and temperature regimes. Evaluate AgroMind performance degradation; develop and test rapid recalibration protocol (few-shot fine-tuning, <3 weeks per new region). Complete dataset curation, documentation, and open release. Submit final validation manuscript.
Potential problems and alternative approaches.
| Activity / Milestone | Q1 '27 | Q2 '27 | Q3 '27 | Q4 '27 | Q1 '28 | Q2 '28 | Q3 '28 | Q4 '28 | Q1 '29 | Q2 '29 | Q3 '29 | Q4 '29 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Aim 1: FL Architecture Dev | ||||||||||||
| IoT Sensor Expansion (50 farms) | ||||||||||||
| Aim 2: Field Trial — Season 1 | ||||||||||||
| Aim 2: Field Trial — Season 2 (crossover) | ||||||||||||
| Aim 3: Dataset Curation | ||||||||||||
| Aim 4: Climate Stress Testing | ||||||||||||
| Open Dataset Release (Zenodo) | ◆ |
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| Publications (target: 4 peer-reviewed) | ◆ |
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| Broader Impacts: PhD Cohort Training |
Technical Development Field Validation Data / Dissemination ◆ Key Milestone
Dr. Carmen Vidal-Roca (PI, 20% effort Y1–3) is Associate Professor of Computational Agronomy at UPV. She holds a PhD in Agronomy (UPV, 2015) and a postdoctoral fellowship at ETH Zürich (2016–2018) in precision agriculture systems. Her 47 peer-reviewed publications (h-index: 22) include foundational work on remote sensing for citrus stress detection and IoT-enabled irrigation control. She has held two prior competitive grants (MICINN PID2021, BBVA Foundation) and supervised 6 completed PhD theses.
Dr. Alejandro Fuentes (Co-IP, 15% effort Y1–3) is Professor of Soil Sciences at UPV with 25 years of field experience in Mediterranean arid zone hydrology. His expertise ensures experimental rigor in soil sensor placement, calibration, and interpretation — directly de-risking the IoT component of AgroMind.
Dra. Priya Sharma (International Collaborator, Purdue, 10% effort Y2–3) is Assistant Professor of Agricultural Informatics at Purdue. Her recent IEEE IoT Journal paper on federated learning for smart farming (35 citations, 2025) establishes her as an ideal collaborator for the FL architecture and California validation components. She brings independent NSF CAREER award funding (NSF 2512834) that partially supports her effort.
| Category | Year 1 | Year 2 | Year 3 | Total (Direct) |
|---|---|---|---|---|
| Personnel (PI + Co-IP + PhD students) | $72,400 | $74,600 | $76,900 | $223,900 |
| Fringe benefits (28%) | $20,272 | $20,888 | $21,532 | $62,692 |
| Equipment (IoT sensors, edge nodes) | $42,000 | $8,000 | $2,000 | $52,000 |
| Travel (conferences + farm visits) | $8,500 | $10,200 | $9,800 | $28,500 |
| Materials & Supplies (cloud compute, reagents) | $18,000 | $14,000 | $11,000 | $43,000 |
| Other (publication fees, participant incentives) | $4,800 | $6,200 | $5,000 | $16,000 |
| Indirect Costs / F&A (52% MTDC, UPV negotiated rate) | $24,100 | $24,900 | $23,658 | $72,658 |
| TOTAL REQUESTED | $190,072 | $158,788 | $149,890 | $498,750 |
Justification highlights: Personnel costs reflect UPV FY2027 salary scales at 20% PI effort, 15% Co-IP effort, and 100% FTE for 4 PhD students (2 funded in Y1, 2 additional in Y2). Equipment ($42K Y1) covers 152 additional IoT soil sensor nodes at $276/node (direct purchase, lowest-cost 3-quote procurement). Cloud compute ($12K/yr) covers GPU hours for FL training on Azure Research Credits supplemented by UPV HPC. Travel ($10K/yr average) funds 2 international conference presentations (NSF PI meetings + IEEE/ACM venues) and quarterly farm visits. All F&A calculated at UPV's federally negotiated 52% MTDC rate (agreement dated March 2026).