The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

种子是农业和工业生产的必然要素。非破坏性水稻种子品种鉴定对于确保水稻纯度和质量至关重要。本研究旨在开发一种基于计算机视觉的系统，利用多种异质特征，利用纹理、外部和物理特性来识别水稻品种。我们在没有任何固定设置的情况下捕获了水稻种子图像，以使系统在工业和农​​民层面都易于使用，这可能会导致图像中的照明问题。为了克服这个问题，我们引入了一种改进的直方图定向梯度 (T20-HOG) 特征，该特征可以描述稻谷图像的光照、尺度和旋转变化。我们还利用了现有的 Haralick 和传统特征，并通过 Lasso 特征选择技术降低了特征的维数。所选特征用于训练前馈神经网络 (FNN) 以预测稻谷品种。实验在两个不同的数据集上进行：BDRICE 和 VNRICE。我们的方法的结果以准确率、准确率、召回率和 F_1 分数四个标准评估指标显示，分别达到了 99.28%、98.64%、98.48% 和 98.56% 的分数。我们还将我们的系统效率与现有研究进行了比较。实验结果表明，我们提出的特征可有效识别水稻品种，并实现了新的最先进的性能。