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A Smartphone-Based Method for Assessing Tomato Nutrient Status through Trichome Density Measurement

Early detection of fertilizer-induced stress in tomato plants is crucial for optimizing crop yield through timely management interventions. While conventional optical methods struggle to detect fertilizer stress in young leaves, these leaves contain valuable diagnostic information through their microscopic hair-like structures, particularly trichomes, which existing approaches have overlooked. This study introduces a smartphone-based noninvasive technique that leverages mobile computing and digital imaging capabilities to quantify trichome density on young leaves with superior detection latency. Our method uniquely combines augmented reality technology with image processing algorithms to analyze trichomes transferred onto specialized measurement paper. A robust automated pipeline processes these images through region extraction, perspective transformation, and illumination correction to precisely quantify trichome density. Validation experiments on hydroponically grown tomatoes under varying fertilizer conditions demonstrated the method's effectiveness. Leave-one-out cross-validation revealed strong predictive performance with the area under the precision-recall curve (PR-AUC: 0.82) and area under the receiver operating characteristic curve (ROC-AUC: 0.64), while the predicted and observed trichome densities exhibited high correlation ($r = 0.79$). This innovative approach transforms smartphones into precise diagnostic tools for plant nutrition assessment, offering a practical, cost-effective solution for precision agriculture.

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