Nondestructive prediction of leaf area in colored cotton cultivars: a comparative approach using machine learning models with an interactive web interface

Abstract Background Leaf area is a crucial indicator of plant growth and physiology, with direct measurements being destructive to the plant. This study aimed to develop and compare machine learning models [support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and deep multilayer perceptron (DMLP)] and linear regression (LRM) for the nondestructive prediction of leaf area in five colored cotton cultivars. A total of 1 334 leaves were sampled, and their length (L), width (W), and leaf area (LA) were determined via digitized images. The models were developed using 70% of the data for training and 30% for validation. Their performance was evaluated using the coefficient of determination ( R 2 ), root mean square error, mean absolute error, mean absolute percentage error, and Willmott's index of agreement. Results The results showed that the machine learning models, notably the ANFIS (triangular membership function), the DMLP (2–16-16–1 configuration), and the SVR [radial basis function (RBF) kernel], significantly outperformed the linear regression models in leaf area estimation accuracy. The ANFIS and DMLP models achieved the highest R 2 (0.979 3, test), followed by the SVR model ( R 2 = 0.979 0, test), all with minimal errors. Among the linear models, the LRM (using the L × W product) was the most effective ( R 2 = 0.978 3). Conclusions On the basis of the performance criteria of the models, the machine learning models are more accurate for the nondestructive estimation of leaf area in colored cotton. The best-performing model (SVR with RBF kernel) was made available in an interactive web application, aiming to optimize crop management with accurate and nondestructive data.