Authors - Qixuan Geng, Chuanchen BI Abstract - Efficient nutrient management is vital in a sugarcane cultivation to sustain the crop yields. But, the conventional practices are still reactive and imprecise often leading to improper nutrient management and yield loss. To overcome this issue, the study utilizes a multimodal AI driven framework by integrating drone-based canopy imaging and in-field soil sensors to aid in real-time nutrient deficiency detection and precise recommendation of fertilizers. UAV images are analysed using a transfer learning based Convolutional Neural Network (CNN) to locate visible deficiency symptoms and determine its severity. In order to forecast impending nutrient deficiencies, significant soil parameters (NPK, moisture, pH, electrical conductivity and temperature) are monitored continuously and processed using GRU/ LSTM- based models. The data and information from sensor networks, images and environmental context are then integrated through a fusion architecture to produce a nutrient deficiency label, severity score, and confidence measure. To ensure interpretability and agronomic safety, predictions are incorporated with crop growth stage- specific nutrient gap model that convert deficiencies into dosages of fertilizers, with alerts given on high-risk conditions and optionally permissioned fertigation control. The proposed system allows proactive, data-driven nutrient management, mitigates the risk of over fertilization, and supports scalable precision agriculture.
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