Image analysis using smartphones: Relationship between leaf color and fresh weight of lettuce under different nutritional treatments

HYESU WON ¹ Dong Sub Kim ^1* Se Eun Lee ² Ji-Hyeon Nam ¹ Jiwon Jung ¹ Yuna Cho ¹ Thomas Evert ³ Noah Kan ³ Steven Kim ³ Dong Sub Kim ^2*

• ¹ Department of Horticulture, Kongju National University, Yesan, Republic of Korea
• ² Kongju National University, Gong, Republic of Korea
• ³ California State University, Monterey Bay, Seaside, California, United States

Image analysis can be useful for assessing crop health and predicting yield. Instead of expensive equipment, smartphones are considered an accessible and low-cost alternative. The objectives of this study were to evaluate whether fresh weight in green and red lettuce could be predicted by leaf color (intensity of green color measured by RGB) under different fertilizer treatments using RGB imaging from two widely used smartphone models (Samsung Galaxy and Apple iPhone). The two smartphones showed similar longitudinal patterns of RGB data (the intensity and dark green proportion), but the absolute difference in the RGB data was significantly different. Therefore, the averaged results were used for the analyses. Color intensity and dark green proportion were associated with the fresh lettuce weight (p = 0.005, 0.003, 0.014 and p < 0.001, respectively). This study suggests that farmers and practitioners can use these economic devices as a non-destructive method to diagnose and monitor the nutritional status and predict lettuce yield.