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Multispectral Imagery Remote Sensing for Monitoring Nutrient Deficiency of Onion (Allium cepa) under Automated Drip Fertigation System in Greenhouse: A Review

Gwendalyn Marie Duca Orzame, Claire Marie Melad Castillo, John Paulo Caraan Sacdalan, Jeannie-Rose Galam Fabula

Abstract


Onion (Allium cepa) is a globally significant crop; however, its productivity is often constrained by inefficient water delivery and poor nutrient management. Advances in precision agriculture technologies, including multispectral remote sensing (MRS) and automated drip fertigation systems, offer promising solutions for sustainable onion cultivation. This review synthesizes current research on the principles, applications, and challenges associated with integrating MRS and fertigation systems in greenhouse environments, with particular emphasis on Israeli-type protected structures, which are known for their high water-use efficiency. Spectral indices such as NDVI, NDRE, GNDVI, and PRI can detect nutrient deficiencies and water stress up to two weeks before visual symptoms appear, thereby enabling timely corrective actions. MRS-guided automated fertigation has been shown to reduce water consumption by up to 25% and improve nutrient-use efficiency by approximately 18% under controlled conditions. However, several challenges remain, including high capital costs, complex data-processing requirements, signal interference due to greenhouse structures, and limited farmer training. This review also highlights key research gaps, such as the need for onion-specific spectral calibration models, robust multimodal data fusion frameworks, and cost-effective precision technologies tailored for developing countries. Overall, precision agriculture holds significant potential to enhance the sustainability and profitability of onion production while contributing to climate-smart agricultural practices.

Keywords



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