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@article{187369,
        author = {Alankar Tripathi and Prof. Raman Pant and Prof. Anil Kumar},
        title = {ML Models to Differentiate Nitrogen Deficiency from Other Nutrient Stress in Plants A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4435-4438},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187369},
        abstract = {Differentiating nitrogen (N) deficiency from other nutrient stresses (e.g., phosphorus, potassium, sulfur, micronutrients) is a critical agricultural task: correct diagnosis leads to targeted fertilizer application, cost savings, and reduced environmental harm. Recent advances in machine learning (ML), remote sensing (hyperspectral/multispectral/UAV), and low-cost imaging have enabled non-destructive nutrient diagnosis, but most work has focused on detecting a single nutrient (often N) rather than distinguishing among multiple nutrient stresses. This review synthesizes literature (2018–2025+) on ML approaches for multi-class nutrient deficiency detection, surveys datasets and sensing modalities, compares methods (classical ML, deep learning, multimodal fusion), discusses evaluation practices, and identifies open challenges and research directions—particularly for reliable differentiation of N deficiency from other nutrient stresses in real-world conditions. Key recommendations include multimodal sensing, targeted data-collection protocols, domain adaptation/federated learning, and explainable models for agronomic adoption.},
        keywords = {nitrogen deficiency, nutrient stress, machine learning, hyperspectral, multispectral, UAV, multi-class classification, precision agriculture},
        month = {November},
        }