In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (×Tritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA = + √ , LA = + (), and LA = + (∕) models had a R 2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models + () and √ × () exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of √ × and √ were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.
