Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Abstract

AbstractIn 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 aestivumL.) and durum wheat (Triticum durumL.) and triticale (×TritosecaleWittm. 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, , , and models had aR2> 90% for all three cultivars (LandWrepresented 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.