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.

中文翻译：

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利用图像处理和人工神经网络估算芝麻的油和蛋白质含量

图像处理在农业的不同领域中有许多应用。本研究旨在使用图像处理技术和人工神经网络（ANN）来估算芝麻基因型的油和蛋白质含量，而无需使用耗时且昂贵的实验室方法。所提出的方法可以准确地估计芝麻中的参数，而不会破坏遗传上有价值的材料。在这项研究中，从125种芝麻基因型的数字图像中提取了138种形态特征。然后，采用多层感知器（MLP）ANN估算油和蛋白质的含量，并确定估算值与实验室测量值之间的关系。将该模型的效率与径向基函数（RBF），扩展RBF（ERBF），GRNN，M5规则，M5树，支持向量机回归和线性回归模型。结果表明，MLP在估计芝麻种质中种子的定性参数方面表现更好。该模型估算的含油量的均方根误差（RMSE）为2.13％（准确度为97.87％），并且R ²为0.93。蛋白质含量的估计RMSE为0.378％（准确度为99.62％），R ²为0.96。