Estimating Oil and Protein Content of Sesame Seeds Using Image Processing and Artificial Neural Network

Abstract

Abstract Image processing has many applications in different fields of agriculture. The present study aimed to use image processing techniques and artificial neural networks (ANN) to estimate oil and protein contents of sesame genotypes without the use of time‐consuming and costly laboratory methods. The proposed method accurately estimates the parameters in sesame seeds without destructing the genetically valuable material. In this study, a set of 138 morphological features were extracted from the digital image of 125 sesame seed genotypes. A multilayer perceptron (MLP) ANN was then employed to estimate oil and protein contents and determine the relationship between estimated values and laboratory‐measured values. The efficiency of this model was compared to radial bases function (RBF), extended RBF (ERBF), GRNN, M5‐Rule, M5‐Tree, support vector machine regression, and linear regression models. Results showed that MLP performed better in estimating qualitative parameters of seeds in the sesame germplasm. The model estimated oil content with an root mean square error (RMSE) of 2.13% (the accuracy of 97.87%) and an R 2 of 0.93. Protein content was estimated by an RMSE of 0.378% (the accuracy of 99.62%) and an R 2 of 0.96.