International Journal of Science, Engineering and TechnologyAuthors: Pawar Ravi Santosh, Prof. M. R. Kumbhoje, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Agriculture faces pressure from changing climate, water scarcity, soil degradation and crop diseases, while many farmers still decide when to irrigate, how much fertilizer to apply and how to treat diseases by experience alone. This paper reviews AI-based smart agriculture with an integrated, field-deployable perspective. It examines IoT sensing of soil moisture, temperature, humidity and nutrients; deep convolutional networks, including efficient architectures such as EfficientNet, for multi-class foliar disease diagnosis; machine learning for soil macronutrient (nitrogen, phosphorus, potassium) classification and fertilizer guidance; and machine learning and reinforcement learning for precision irrigation scheduling based on evapotranspiration and weather forecasts. Deployment on resource-constrained edge gateways using model quantization is discussed so that diagnoses and irrigation decisions do not depend on continuous internet access. A five-layer architecture comprising field sensing, edge intelligence, a farm decision engine, actuation and a farmer advisory application is proposed, together with research gaps, objectives, a comparison of AI components and challenges such as cost, data scarcity, connectivity, farmer adoption and sustainability. The review concludes that integrated AI systems can raise yields while saving water, fertilizer and pesticide.
DOI: https://doi.org/10.5281/zenodo.24214845
