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WOS: 000298656100019 Leaf area is one of the most important parameter for plant growth. Reliable equations were offered to predict leaf area for Zea mays L. cultivars. All equations produced for leaf area were derived as affected by leaf length and leaf width. As a result of ANOVA and multiregression analysis, it was found that there was a close relationship between actual and predicted growth parameters. The produced leaf-area prediction model in the present study is LA = a + b L + c W + d LZ where LA is leaf area, L is leaf length, W is maximum leaf width, LZ is leaf zone and a, b, c, d are coefficients. R (2) values were between 0.88-0.97 and standard errors were found to be significant at the p < 0.001 significance level.

Six leaf samplings were conducted in two sunflower (Helianthus annuus L.) hybrids during the 2006 growing season in order to evaluate a simple model proposed for leaf area (LA) estimation. A total of 144 leaves were processed using an image analysis system and LA, maximum leaf width (W) [cm], and midvein length (L) [cm] were measured. Also, LA was estimated using the model proposed by Rouphael et al. (2007). Measured LA was exponentially related with L and W, and the W-LA relationships showed higher r 2. Estimated LA was strongly and exponentially related with L. Strong, linear relationships with high r 2 between estimated and measured LA confirmed the high predictability of the proposed model.

Heteroblasty of sugar beet cultivar Rizor was studied under field conditions for three growing seasons (2003, 2005, 2006) in a Randomized Complete Block (RCB) design experiment. Eleven leaf samplings, from early June till the end of October, were conducted each year and leaf shape parameters [leaf area (LA), centroid X or Y (CX or CY), length (L), width (W), average radial (AR), elongation (EL), shape factor (SF)] were determined by an image analysis system. During samplings, Leaf Area Index (LAI) was measured non-destructively. Significant year and sampling effects were found for all traits determined. With the progress of the growing season, leaves became smaller (LA, L, W, and AR were decreased) and rounded. The largest leaves were sampled in 2006 when LAI was highest. LA was strongly correlated with L and W with simple functions (y = 0.1933 x2.2238, r 2 = 0.96, p

In two successive years (2003 and 2004), a set of 16 commercial sugar beet cultivars was established in Randomized Complete Block experiments at two sites in central Greece. Cultivar combination was different between years, but not between sites. Leaf sampling took place once during the growing season and leaf area, LA [cm2], leaf midvein length, L [cm] and maximum leaf width, W [cm] were determined using an image analysis system. Leaf parameters were mainly affected by cultivars. Leaf dimensions and their squares (L2, W2) did not provide an accurate model for LA predictions. Using L×W as an independent variable, a quadratic model (y = 0.003 x2 − 1.3027 x + 296.84, r 2 = 0.970, p

An indirect method of leaf area measurement for Rizor sugar beet cultivar was tested. Leaves were sampled during two growing seasons in a Randomised Complete Block Design experiment. For 2002 samplings, leaf area [cm2] was linearly correlated with maximum leaf width [cm] using all leaf samples (r2 = 0.83, p < 0.001) or using the means of the 8 sampling occasions (r2 = 0.97, p < 0.001). Correlations between leaf area and leaf mid vein length [cm] were weaker (r2 = 0.75, p < 0.001 and r2 = 0.93, p < 0. 001, respectively). For 2003 samplings, the area estimated by the equations was highly correlated to the measured leaf area.

In a two-year experiment (2002–2003), five N application rates [0, 60, 120, 180, and 240 kg(N) ha−1, marked N0, N60, N120, N180, and N240, respectively] were applied to sugar beet cv. Rizor arranged in a Randomized Complete Block design with six replications. Leaf shape parameters [leaf area (LA), maximum length (L), maximum width (W), average radial (AR), elongation (EL), and shape factor (SF)] were determined using an image analysis system, and leaf area index (LAI) was non-destructively measured every two weeks, from early August till mid-September (four times). Years, samplings, and their interaction had significant effects on the determined parameters. Fertilization at the highest dose (N240) increased L and sampling×fertilization interaction had significant effects on LA, L, W, and SF. For this interaction, W was the best-correlated parameter with LA and LAI meaning that W is a good predictor of these parameters. Two proposed models for LA estimation were tested. The model based on both leaf dimensions [LA = 0.5083 (L×W) + 31.928] predicted LA better than that using only W (LA = 21.686 W − 112.88). Instrumentally measured LAI was highly correlated with predicted LAI values derived from a quadratic function [LAI = −0.00001 (LA)2 + 0.0327 LA − 2.0413]. Thus, both LA and LAI can be reliably predicted non-destructively by using easily applied functions based on leaf dimensions (L, W) and LA estimations, respectively.