Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency

TL;DR

Fusion Intelligence (FI) proposes a bio-inspired framework that integrates insect-based natural intelligence (NI) with AI to create adaptive, energy-efficient IoT systems. The architecture combines an NI subsystem (living entities, sensors, actuators, control and PMU) with an AI subsystem (data processing, a Model Supervisor, and separate monitoring/controlling models) to enable real-time perception, decision-making, and actuation. In a simulated pollination case, FI leverages Beehave/Beescout tools to guide bees via AI-driven adjustments and artificial food patches, achieving approximately a 50% improvement in pollination potential as measured by the Pollination Improvement Index (PII) and confirming strong predictive performance (R^2 ≈ 0.88; CNN accuracy ≈ 0.90). The study discusses limitations of current simulators, ethical considerations, and outlines future directions toward experimental validation and broader FI applications. Overall, FI offers a blueprint for combining biological efficiency with digital intelligence to boost agricultural productivity and inform cross-domain intelligent systems.

Abstract

This paper introduces Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms such as bees and ants are integrated with the computational power of Artificial Intelligence (AI). This interdisciplinary field seeks to create systems that are not only smart but also adaptive and responsive in ways that mimic the nature. As FI evolves, it holds the promise of revolutionizing the way we approach complex problems, leveraging the best of both biological and digital worlds to create solutions that are more effective, sustainable, and harmonious with the environment. We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily).

Figures (4)

• Figure 1: Flow chart of the NI and AI subsystems used within the FI system architecture. (a) The NI subsystem hosts the biological entities that act as sensors and actuators in the environment. Compatible electromechanical sensors observe these entities. The control unit digitizes the sensor outputs, sends them to the AI subsystem, and decodes its instructions. The latter are relayed to the entities via compatible actuation devices like speakers or food dispensers. (b) The AI subsystem hosts the digital processes required to interpret the actions of biological entities and alter their behaviour based on user objectives. The Supervisor compares the observed and required outcomes to manage the AI model outputs in real-time based on user-defined control algorithms.
• Figure 2: Block diagram of the AI implemented for the FI pollination system. The monitoring model is trained to recognise patch locations that are not visited by the bees. The controlling model takes this information and adds artificial patches to guide the bees towards un-visited patches.
• Figure 3: Simulated bee foraging activity over the landscape in the baseline (left) and FI-enabled (right) scenarios. The yellow rectangular patches represent the flower/crop under cultivation. The smaller red patches represent artificial food patches placed between the crops enabled by the FI system to guide the bees.
• Figure 4: Comparison of simulation metrics in baseline and FI-enabled scenarios for evaluating pollination efficiency. FI increased coverage of the total field area to 96%. Similarly, the fraction of detected patches increased to 95%. We see FI improving foraging period to 63% taking control of the environment. The resulting improvement in visitation metrics results in average number of trips per hour and average number of completed foraging trips increasing to 35% and 27% respectively. Due to improved visitation, the number of total daily visits increases to 41%.