<![CDATA[Durian (Durio zibethinus), particularly the cultivars Monthong and Bawor, is a leading horticultural commodity with high economic value. Accurate leaf area estimation is essential for supporting physiological studies and plant growth modeling. However, conventional measurement methods are often characterized by their slow and destructive nature. This study aimed to analyze and identify the constant (k) values of the leaves of durian cultivars Monthong and Bawor using a digital image processing approach. A total of 40 leaf samples from each cultivar were analyzed. Image acquisition was performed using a smartphone camera, while image processing and leaf area measurement were conducted with the ImageJ software. The leaf constant was calculated as the ratio of the digitally measured leaf area to the product of manually measured leaf length and width. The results showed that the mean leaf constant for Monthong durian was 0.702, while for Bawor durian, it was 0.691. These results exhibited narrow value distributions, devoid of any outliers. The correlation between the measured and predicted leaf area yielded very high coefficients of determination (R² of 0.997 for cultivar Monthong and R² of 0.999 for cultivar Bawor). Further statistical evaluation confirmed that the predictive model had very high accuracy, evidenced by its low RMSE values (≤ 1.059), an NRMSE of 0.01, an NSE of at least 0.997, and a Willmott’s index of agreement (d) of at least 0.999. These results indicate that leaf constant values derived from digital image processing can generate precise leaf area estimates and offer a fast, efficient, and non-destructive alternative to conventional measurement methods. In practical terms, this approach enhances precision agriculture by enabling more accurate monitoring of leaf growth dynamics, which is essential for crop management and yield optimization. This finding presents opportunities for further application across other durian cultivars and the broader adoption of similar methods in other plant commodities within the context of precision agriculture and plant growth modeling.]]>
