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Deploying AI decision systems in floor‑rearing farms promises improved efficiency but faces practical hurdles. Three major challenges emerge: data infrastructure gaps, worker acceptance, and system maintenance cost. Many farms lack reliable sensors or stable internet, leading to incomplete data. Workers, accustomed to manual observation, often distrust AI recommendations.
Solutions include phased deployment starting with basic environmental monitoring to build trust. Installing ruggedized, low‑cost sensors that work offline and sync later addresses connectivity issues. Conducting hands‑on training that compares AI advice with traditional methods helps workers understand value. Hybrid models where AI suggests actions but leaves final decisions to managers reduce resistance.
Additionally, leasing equipment with maintenance contracts lowers upfront investment, while cloud‑based analytics reduce on‑site computing needs. Successful implementation requires not just technology but cultural change, turning skepticism into collaboration.
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