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

• URL: http://arxiv.org/abs/2404.19513v1
• Date: Tue, 30 Apr 2024 12:45:41 GMT
• Title: A Smartphone-Based Method for Assessing Tomato Nutrient Status through Trichome Density Measurement
• Authors: Sho Ueda, Xujun Ye,
• Abstract summary: Accurately identifying fertilizer-induced stress through the morphological traits of tomato plants has become a critical agricultural challenge. We developed a simple and cost-effective smartphone-based method for measuring trichome density. Our results indicate that our novel method for measuring trichome density accurately reflects fertilizer stress in tomato plants.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately assessing tomato plant nutrient status is crucial for maintaining high yields. Consequently, accurately identifying fertilizer-induced stress through the morphological traits of tomato plants has become a critical agricultural challenge. Research and development efforts have focused on developing noninvasive diagnostic tools for nutrition that leverage a combination of morphological traits and advanced sensor technologies. Given these advancements, detecting fertilizer stress by observing morphological traits near the growth points of tomatoes is still a significant challenge. To address this challenge, we developed a simple and cost-effective smartphone-based method for measuring trichome density. This method involves transferring trichomes from the surface of a leaf onto cellophane tape and capturing images using a smartphone. The images are processed using computer vision techniques to calculate the trichome density. To assess the efficacy of this method, we performed experiments on hydroponically grown tomato plants subjected to varying fertilizer concentrations. Our results indicate that our novel method for measuring trichome density accurately reflects fertilizer stress in tomato plants. The predictive performance of our model, as evaluated by the mean area under the precision recall curve, was 0.824, despite variations in the measurement data caused by differences in optical conditions. This study introduces an innovative approach for designing diagnostic devices for detecting fertilizer stress in plants by considering the surface structures of plants. Our proposed method represents a straightforward, efficient, and economical approach for evaluating the nutrient status of tomato plants and has the potential to overcome the limitations of conventional noncontact optical methods.

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