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@article{191873,
        author = {Badgujar Hitesh Ganesh and Kolhe Ashwini Ashish and Marathe Jaya Kailas and Pawar Mansi Dines},
        title = {An IoT-Enabled AI Framework for Real-Time Crop Advisory and Decision Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8044-8055},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191873},
        abstract = {This paper proposes an AI and IOT-based Crop Advisor integrated with a chatbot interface to revolutionize smart agriculture, by combining AIIOT and conversational interfaces, the system demonstrates its potential as an inclusive and efficient approach to modern agriculture... IOT sensors deployed in the field continuously monitor key environmental parameters such as soil moisture, temperature, humidity, pH, and nutrient levels. The real-time data is processed using machine learning algorithms to recommend the most suitable crops and provide timely agricultural advice. The system has been evaluated for Toor Dal (Pigeon Pea) and Wheat, two widely cultivated crops with distinct growth requirements. Toor Dal thrives in moderately fertile soils with a pH of 6.0–7.5 and temperature between 25–35 °C. As a legume, it fixes atmospheric nitrogen and thus requires relatively lower nitrogen supplementation (20–25 kg/ha) but responds strongly to phosphorus (50–60 kg/ha) and potassium (20–25 kg/ha) for improved root development and pod formation. Wheat, in contrast, grows best under cooler conditions with a temperature range of 15–25 °C, soil pH between 6.0–7.0, and higher soil moisture levels of 60–80%. It requires substantial nitrogen input (100– 120 kg/ha) for grain development, along with phosphorus (50–60 kg/ha) and potassium (40–50 kg/ha) for root establishment and disease resistance. Maintaining a balanced nutrient supply is essential, as nutrient deficiencies delay growth while excess nitrogen increases susceptibility to pests and fungal infestations. Preventive strategies such as crop rotation, balanced Nitrogen(N), Phosphorous(P), Potassium(K) fertilization, use of resistant seed varieties, biological pest control, and timely irrigation management are integrated into the advisory framework to minimize plant and stem diseases. A user-friendly chatbot interface enables farmers to interact with the system in natural language, asking questions related to crop suitability, irrigation schedules, fertilizer recommendations, and pest or disease management. With a response time of less than 1.5 seconds and a crop prediction accuracy of 92%, the proposed system empowers farmers with personalized and accessible recommendations, enhances crop yield, reduces resource wastage, and bridges the digital divide in rural communities.},
        keywords = {IOT, Crop, Fertilizer, Chat Bot, Irrigation and Machine Learning},
        month = {January},
        }