Smart Farm Voice Assistant

Agriculture continues to be the backbone of the economy in many developing nations, yet smallholder farmers face persistent challenges in managing irrigation and pest control. Traditional irrigation methods are mostly based on guesswork or fixed schedules, often leading to water wastage or insufficient watering. similarly, pest and disease infestations are usually identified too late, resulting in major crop losses and excessive reliance on chemical pesticides. While modern precision farming system exists, they are often too expensive, too complex, or inaccessible to farmers with limited digital literacy. To overcome these barriers, this paper proposes a smart farm voice assistant, an integrated solution that combines internet of things (IOT)devices, Artificial intelligence and Machine Learning (AI/ML), and Natural Language Processing (NLP). The system collects real-time soil moisture and weather data through sensor and uses an AI model to determine when irrigate is required. Farmers can also capture crop leaf images, which are analyzed by a pest detection model trained on plant disease dataset. Instead of presenting technical data, the system provides direct, actionable voice instructions in the local language, such as “irrigate today evening” or “spray neem oil for pest control”. The key novelty of this approach lies in its ability to combine irrigation scheduling and pest advisory in a single, low-cost framework while ensuring accessibility through voice interaction rather than text-based alerts. The proposed voice assistance, bridging the gap between technology and farmers real needs.