Won Hye Su, Lee Eunji, Lee Seeun, Nam Ji-Hyeon, Jung Jiwon, Cho Yuna, Evert Thomas, Kan Noah, Kim Steven, Kim Dong Sub
Department of Horticulture, Kongju National University, Yesan, Republic of Korea.
Department of Biological Science, Kongju National University, Gongju, Republic of Korea.
Front Plant Sci. 2025 May 5;16:1589825. doi: 10.3389/fpls.2025.1589825. eCollection 2025.
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.
图像分析有助于评估作物健康状况并预测产量。智能手机被视为一种便捷且低成本的选择,而非昂贵的设备。本研究的目的是使用两款广泛使用的智能手机型号(三星Galaxy和苹果iPhone)进行RGB成像,评估在不同肥料处理下,绿色和红色生菜的鲜重是否可以通过叶片颜色(由RGB测量的绿色强度)来预测。这两款智能手机显示出相似的RGB数据纵向模式(强度和深绿色比例),但RGB数据的绝对差异显著不同。因此,分析使用了平均结果。颜色强度和深绿色比例与生菜鲜重相关(p分别为0.005、0.003、0.014和p<0.001)。本研究表明,农民和从业者可以使用这些经济实惠的设备作为一种无损方法来诊断和监测营养状况并预测生菜产量。