Among many indirect approaches to retrieve effective leaf area index (LAI), hemispherical photography is now widely used by the scientific community in forestry applications. A recent software (CAN_EYE) is used to estimate effective and true LAI from unidirectional gap fractions measured in crops. The effective LAI is computed with the Poisson law whereas the true LAI is estimated introducing a clumping index in the Poisson law. The clumping index estimation is based on the Lang and Xiang averaging method. CAN_EYE includes an automatic image classification and allows the processing of series of photographs which is mandatory to sample the spatial variability of the canopy. The objective of this study is to determine if the use of the clumping index in the gap fraction formulation improves seasonal LAI estimates of crops. Hemispherical photographs were taken throughout two growing seasons over wheat, sunflower and maize canopies. CAN_EYE LAI estimates were then compared to destructive LAI. The conditions under which photographs were acquired and processed are discussed. For the three crops studied here, the minimum distance required between camera and canopy is 1 m. When feasible, there is a clear advantage in acquiring the images from above canopies and on overcast days to facilitate the image classification. For wheat and sunflower, the best LAI estimates are assessed with effective LAI (RMSE of 0.15, y = 0.9540x for wheat and RMSE of 0.38, y = 0.8427x for sunflower). For maize, the best LAI estimates are obtained using the clumping index (RMSE of 0.39 and y = 0.9010x). Despite good fits between CAN_EYE and destructive LAI estimates, compensation effects between leaf area index and leaf angle distribution may occur during the inversion procedure. Moreover, values of clumping index given by CAN_EYE are in certain cases correlated with the size of the cells used to divide photographs. The Lang and Xiang averaging method introduced into CAN-EYE should be improved.