Energy-Efficient Multimodal AI for Optimizing Crop Yields in Smart Agriculture

1) We proposed Dual CMAB(Contextual Multi-Armed Bandit),a two-level bandit framework decoupling sensor-level energy management (CMAB-1) from model-level inference selection (CMAB-2) for end-to-end energy optimisation in a multimodal agriculture pipeline. CMAB-1 is a two-arm policy: Arm-0 activates only tabular sensors for irrigation and fertilizer decisions; Arm-1 additionally activates the camera for image-based disease detection.

2) We train MobileNetV2 and ResNet34 on the 9-class Paddy Doctor dataset, and build XGBoost classifiers for irrigation scheduling and fertilizer recommendation.

3) We quantify energy consumption using real GPU measurements on an NVIDIA GeForce RTX 5060 Ti (mil-
lijoules per sample), demonstrating 23.1 % real energy savings versus always ResNet34 and 88.6 % simulated
savings versus a static always-on pipeline, with negligible accuracy degradation.