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A personal research project implemented a sparse federated learning system to orchestrate a smart agriculture microgrid on a small organic farm. The system uses sparse autoencoders running on low-power microcontrollers (ESP32-class) to encode sensor data into compact embeddings. These sparse model updates are communicated efficiently over low-bandwidth LoRaWAN networks to a central Raspberry Pi aggregator, which predicts irrigation needs and balances energy distribution from solar panels and batteries. The approach reduces communication by 92%, extends node battery life from 2 weeks to over 3 months, and achieves irrigation prediction accuracy within 5% of centralized models. Techniques include weight pruning, integer quantization, adaptive sparsity, clustered federated learning for non-IID data, and cyclical pruning schedules to maintain model performance under resource constraints.