Abstract: Sugarcane's overall productivity, yield, and crop health are all greatly impacted by nutrient deficiencies. Manual observation and laboratory testing are the mainstays of traditional methods for detecting these deficiencies, but they are costly, time-consuming, and frequently subject to human error. Furthermore, it can be difficult to make an accurate diagnosis because the visual symptoms of various nutrient deficiencies often overlap. This study suggests a deep learning-based method for automatically identifying nutrient deficiencies in sugarcane through image analysis in order to overcome these drawbacks. In order to accurately identify deficiencies like nitrogen, phosphorus, and potassium shortages, Convolutional Neural Networks (CNNs) are used to extract and classify features from images of sugarcane leaves. By offering scalable, precise, and real-time solutions, the suggested system improves efficiency by lowering reliance on laboratory testing and expert knowledge. By incorporating artificial intelligence into The goals of precision agriculture are to enhance crop management, maximise fertiliser use, and advance environmentally friendly farming methods. Results from experiments show how well deep learning models identify and categorise nutrient deficiencies, indicating their potential for practical agricultural uses.
Keywords: Disease detection, Image Processing, Deep Learning.
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DOI:
10.17148/IJARCCE.2025.14622
